http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Jdhchow&feedformat=atomstatwiki - User contributions [US]2022-05-28T02:38:36ZUser contributionsMediaWiki 1.28.3http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Speech2Face:_Learning_the_Face_Behind_a_Voice&diff=48518Speech2Face: Learning the Face Behind a Voice2020-11-30T20:27:46Z<p>Jdhchow: </p>
<hr />
<div>== Presented by == <br />
Ian Cheung, Russell Parco, Scholar Sun, Jacky Yao, Daniel Zhang<br />
<br />
== Introduction ==<br />
This paper presents a deep neural network architecture called Speech2Face. This architecture utilizes millions of Internet/Youtube videos of people speaking to learn the correlation between a voice and the respective face. The model learns the correlations, allowing it to produce facial reconstruction images that capture specific physical attributes, such as a person's age, gender, or ethnicity, through a self-supervised procedure. Namely, the model utilizes the simultaneous occurrence of faces and speech in videos and does not need to model the attributes explicitly. The model is evaluated and numerically quantifies how closely the reconstruction, done by the Speech2Face model, resembles the true face images of the respective speakers.<br />
<br />
== Previous Work ==<br />
With visual and audio signals being so dominant and accessible in our daily life, there has been huge interest in how visual and audio perceptions interact with each other. Arandjelovic and Zisserman [1] leveraged the existing database of mp4 files to learn a generic audio representation to classify whether a video frame and an audio clip correspond to each other. These learned audio-visual representations have been used in a variety of setting, including cross-modal retrieval, sound source localization and sound source separation. This also paved the path for specifically studying the association between faces and voices of agents in the field of computer vision. In particular, cross-modal signals extracted from faces and voices have been proposed as a binary or multi-task classification task and there have been some promising results. Studies have been able to identify active speakers of a video, separate speech from multiple concurrent sources, predict lip motion from speech, and even learn the emotion of the agents based on their voices.<br />
<br />
Recently, various methods have been suggested to use various audio signals to reconstruct visual information, where the reconstructed subject is subjected to a priori. Notably, Duarte et al. [2] were able to synthesize the exact face images and expression of an agent from speech using a GAN model. This paper instead hopes to recover the dominant and generic facial structure from a speech.<br />
<br />
== Motivation ==<br />
It seems to be a common trait among humans to imagine what some people look like when we hear their voices before we have seen what they look lke. There is a strong connection between speech and appearance, which is a direct result of the factors that affect speech, including age, gender, and facial bone structure. In addition, other voice-appearance correlations stem from the way in which we talk: language, accent, speed, pronunciations, etc. These properties of speech are often common among many different nationalities and cultures, which can, in turn, translate to common physical features among different voices. Namely, from an input audio segment of a person speaking, the method would reconstruct an image of the person’s face in a canonical form (frontal-facing, neutral expression). The goal was to study to what extent people can infer how someone else looks from the way they talk. Rather than predicting a recognizable image of the exact face, the authors were more interested in capturing the dominant facial features.<br />
<br />
== Model Architecture == <br />
<br />
'''Speech2Face model and training pipeline'''<br />
<br />
[[File:ModelFramework.jpg|center]]<br />
<br />
<div style="text-align:center;"> Figure 1. '''Speech2Face model and training pipeline''' </div><br />
<br />
<br />
<br />
The Speech2Face Model used to achieve the desired result consist of 2 parts - a voice encoder which takes in a spectrogram of speech as input and outputs low dimensional face features, and a face decoder which takes in face features as input and outputs a normalized image of a face (neutral expression, looking forward). Figure 1 gives a visual representation of the pipeline of the entire model, from video input to a recognizable face. The face decoder itself was taken from previous work by Cole et al [3] and will not be explored in great detail here, but in essence the facenet model is combined with a single multilayer perceptron layer, the result of which is passed through a convolutional neural network to determine the texture of the image, and a multilayer perception to determine the landmark locations. The two results are combined to form an image. This model was trained using the VGG-Face model as input. It was also trained separately and remained fixed during the voice encoder training. The variability in facial expressions, head positions and lighting conditions of the face images creates a challenge to both the design and training of the Speech2Face model. To avoid this problem the model is trained to first regress to a low dimensional intermediate representation of the face. The VGG-Face model, a face recognition model that is pretrained on a largescale face database [5] is used to extract a 4069-D face feature from the penultimate layer of the network. <br />
<br />
'''Voice Encoder Architecture''' <br />
<br />
[[File:VoiceEncoderArch.JPG|center]]<br />
<br />
<div style="text-align:center;"> Table 1: '''Voice encoder architecture''' </div><br />
<br />
<br />
<br />
The voice encoder itself is a convolutional neural network, which transforms the input spectrogram into pseudo face features. The exact architecture is given in Table 1. The model alternates between convolution, ReLU, batch normalization layers, and layers of max-pooling. In each max-pooling layer, pooling is only done along the temporal dimension of the data. This is to ensure that the frequency, an important factor in determining vocal characteristics such as tone, is preserved. In the final pooling layer, an average pooling is applied along the temporal dimension. This allows the model to aggregate information over time and allows the model to be used for input speeches of varying lengths. Two fully connected layers at the end are used to return a 4096-dimensional facial feature output.<br />
<br />
'''Training'''<br />
<br />
The AVSSpeech dataset, a large-scale audio-visual dataset is used for the training. AVSSpeech dataset is comprised of millions of video segments from Youtube with over 100,000 different people. The training data is composed of educational videos and does not provide an accurate representation of the global population, which will clearly affect the model. Also note that facial features that are irrelevant to speech, like hair color, may be predicted by the model. From each video, a 224x224 pixels image of the face was passed through the face decoder to compute a facial feature vector. Combined with a spectrogram of the audio, a training and test set of 1.7 and 0.15 million entries respectively were constructed.<br />
<br />
The voice encoder is trained in a self-supervised manner. A frame that contains the face is extracted from each video and then inputted to the VGG-Face model to extract the feature vector <math>v_f</math>, the 4096-dimensional facial feature vector given by the face decoder on a single frame from the input video. This provides the supervision signal for the voice-encoder. The feature <math>v_s</math>, the 4096 dimensional facial feature vector from the voice encoder, is trained to predict <math>v_f</math>.<br />
<br />
In order to train this model, a proper loss function must be defined. The L1 norm of the difference between <math>v_s</math> and <math>v_f</math>, given by <math>||v_f - v_s||_1</math>, may seem like a suitable loss function, but in actuality results in unstable results and long training times. Figure 2, below, shows the difference in predicted facial features given by <math>||v_f - v_s||_1</math> and the following loss. Based on the work of Castrejon et al. [4], a loss function is used which penalizes the differences in the last layer of the face decoder <math>f_{VGG}</math> and the first layer <math>f_{dec}</math>. The final loss function is given by: $$L_{total} = ||f_{dec}(v_f) - f_{dec}(v_s)|| + \lambda_1||\frac{v_f}{||v_f||} - \frac{v_s}{||v_s||}||^2_2 + \lambda_2 L_{distill}(f_{VGG}(v_f), f_{VGG}(v_s))$$<br />
This loss penalizes on both the normalized Euclidean distance between the 2 facial feature vectors and the knowledge distillation loss, which is given by: $$L_{distill}(a,b) = -\sum_ip_{(i)}(a)\text{log}p_{(i)}(b)$$ $$p_{(i)}(a) = \frac{\text{exp}(a_i/T)}{\sum_j \text{exp}(a_j/T)}$$ Knowledge distillation is used as an alternative to Cross-Entropy. By recommendation of Cole et al [3], <math> T = 2 </math> was used to ensure a smooth activation. <math>\lambda_1 = 0.025</math> and <math>\lambda_2 = 200</math> were chosen so that magnitude of the gradient of each term with respect to <math>v_s</math> are of similar scale at the <math>1000^{th}</math> iteration.<br />
<br />
<center><br />
[[File:L1vsTotalLoss.png | 700px]]<br />
</center><br />
<br />
<div style="text-align:center;"> Figure 2: '''Qualitative results on the AVSpeech test set''' </div><br />
<br />
== Results ==<br />
<br />
'''Confusion Matrix and Dataset statistics'''<br />
<br />
<center><br />
[[File:Confusionmatrix.png| 600px]]<br />
</center><br />
<br />
<div style="text-align:center;"> Figure 3. '''Facial attribute evaluation''' </div><br />
<br />
<br />
<br />
In order to determine the similarity between the generated images and the ground truth, a commercial service known as Face++ which classifies faces for distinct attributes (such as gender, ethnicity, etc) was used. Figure 3 gives a confusion matrix based on gender, ethnicity, and age. By examining these matrices, it is seen that the Speech2Face model performs very well on gender, only misclassifying 6% of the time. Similarly, the model performs fairly well on ethnicities, especially with white or Asian faces. Although the model performs worse on black and Indian faces, that can be attributed to the vastly unbalanced data, where 50% of the data represented a white face, and 80% represented a white or Asian face. <br />
<br />
'''Feature Similarity'''<br />
<br />
<center><br />
[[File:FeatSim.JPG]]<br />
</center><br />
<br />
<div style="text-align:center;"> Table 2. '''Feature similarity''' </div><br />
<br />
<br />
<br />
Another examination of the result is the similarity of features predicted by the Speech2Face model. The cosine, L1, and L2 distance between the facial feature vector produced by the model and the true facial feature vector from the face decoder were computed, and presented, above, in Table 2. A comparison of facial similarity was also done based on the length of audio input. From the table, it is evident that the 6-second audio produced a lower cosine, L1, and L2 distance, resulting in a facial feature vector that is closer to the ground truth. <br />
<br />
'''S2f -> Face retrieval performance'''<br />
<br />
<center><br />
[[File: Retrieval.JPG]]<br />
</center><br />
<br />
<div style="text-align:center;"> Table 3. '''S2F -> Face retrieval performance''' </div><br />
<br />
<br />
<br />
The performance of the model was also examined on how well it could produce the original image. The R@K metric, also known as retrieval performance by recall at K, was developed in which the K closest images in distance to the output of the model are found, and the chance that the original image is within those K images is the R@K score. A higher R@K score indicates better performance. From Table 3, above, we see that both the 3-second and 6-second audio showed significant improvement over random chance, with the 6-second audio performing slightly better.<br />
<br />
== Conclusion ==<br />
The report presented a novel study of face reconstruction from audio recordings of a person speaking. The model was demonstrated to be able to predict plausible face reconstructions with similar facial features to real images of the person speaking. The problem was addressed by learning to align the feature space of speech to that of a pretrained face decoder. The model was trained on millions of videos of people speaking from YouTube. The model was then evaluated by comparing the reconstructed faces with a commercial facial detection service. The authors believe that facial reconstruction allows a more comprehensive view of voice-face correlation compared to predicting individual features, which may lead to new research opportunities and applications.<br />
<br />
== Discussion and Critiques ==<br />
<br />
There is evidence that the results of the model may be heavily influenced by external factors:<br />
<br />
1. Their method of sampling random YouTube videos resulted in an unbalanced sample in terms of ethnicity. Over half of the samples were white. We also saw a large bias in the model's prediction of ethnicity towards white. The bias in the results shows that the model may be overfitting the training data and puts into question what the performance of the model would be when trained and tested on a balanced dataset. <br />
<br />
2. The model was shown to infer different face features based on language. This puts into question how heavily the model depends on the spoken language. The paper mentioned the quality of face reconstruction may be affected by uncommon languages, where English is the most popular language on Youtube(training set). Testing a more controlled sample where all speech recording was of the same language may help address this concern to determine the model's reliance on spoken language.<br />
<br />
3. The evaluation of the result is also highly dependent on the Face++ classifiers. Since they compare the age, gender, and ethnicity by running the Face++ classifiers on the original images and the reconstructions to evaluate their model, the model that they create can only be as good as the one they are using to evaluate it. Therefore, any limitations of the Face++ classifier may become a limitation of Speech2Face and may result in a compounding effect on the miss-classification rate. <br />
<br />
4. Figure 4.b shows the AVSpeech dataset statistics. However, it doesn't show the statistics about speakers' ethnicity and the language of the video. If we train the model with a more comprehensive dataset that includes enough Asian/Indian English speakers and native language speakers will this increase the accuracy?<br />
<br />
5. One concern about the source of the training data, i.e. the Youtube videos, is that resolution varies a lot since the videos are randomly selected. That may be the reason why the proposed model performs badly on some certain features. For example, it is hard to tell the age when the resolution is bad because the wrinkles on the face are neglected.<br />
<br />
6. The topic of this project is very interesting, but I highly doubt this model will be practical in real world problems. Because there are many factors to affect a person's sound in the real world environment. Sounds such like phone clock, TV, car horn and so on. These sounds will decrease the accuracy of the predicted result of the model.<br />
<br />
7. A lot of information can be obtained from someone's voice, this can potentially be useful for detective work and crime scene investigation. In our world of increasing surveillance, public voice recording is quite common and we can reconstruct images of potential suspects based on their voice. In order for this to be achieved, the model has to be thoroughly trained and tested to avoid false positives as it could have highly destructive outcome for a falsely convicted suspect.<br />
<br />
8. This is a very interesting topic, and this summary has a good structure for readers. Since this model uses Youtube to train model, but I think one problem is that most of the YouTubers are adult, and there are many additional reasons that make this dataset be highly unbalanced. What is more, some people may have a baby voice, this also could affect the performance of the model. But overall, this is a meaningful topic, it might help police to locate the suspects. So it might be interesting to apply this to the police.<br />
<br />
9. In addition, it seems very unlikely that any results coming from this model would ever be held in a regard even remotely close to being admissible in court to identify a person of interest until the results are improved and the model can be shown to work in real world applications. Otherwise, there seems to be very little use for such technology and it could have negative impacts on people if they were to be depicted in an unflattering way by the model based on their voice.<br />
<br />
== References ==<br />
[1] R. Arandjelovic and A. Zisserman. Look, listen and learn. In<br />
IEEE International Conference on Computer Vision (ICCV),<br />
2017.<br />
<br />
[2] A. Duarte, F. Roldan, M. Tubau, J. Escur, S. Pascual, A. Salvador, E. Mohedano, K. McGuinness, J. Torres, and X. Giroi-Nieto. Wav2Pix: speech-conditioned face generation using generative adversarial networks. In IEEE International<br />
Conference on Acoustics, Speech and Signal Processing<br />
(ICASSP), 2019.<br />
<br />
[3] F. Cole, D. Belanger, D. Krishnan, A. Sarna, I. Mosseri, and W. T. Freeman. Synthesizing normalized faces from facial identity features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.<br />
<br />
[4] L. Castrejon, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba. Learning aligned cross-modal representations from weakly aligned data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.<br />
<br />
[5] O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In British Machine Vision Conference (BMVC), 2015.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Speech2Face:_Learning_the_Face_Behind_a_Voice&diff=48517Speech2Face: Learning the Face Behind a Voice2020-11-30T20:26:45Z<p>Jdhchow: </p>
<hr />
<div>== Presented by == <br />
Ian Cheung, Russell Parco, Scholar Sun, Jacky Yao, Daniel Zhang<br />
<br />
== Introduction ==<br />
This paper presents a deep neural network architecture called Speech2Face. This architecture utilizes millions of Internet/Youtube videos of people speaking to learn the correlation between a voice and the respective face. The model learns the correlations, allowing it to produce facial reconstruction images that capture specific physical attributes, such as a person's age, gender, or ethnicity, through a self-supervised procedure. Namely, the model utilizes the simultaneous occurrence of faces and speech in videos and does not need to model the attributes explicitly. The model is evaluated and numerically quantifies how closely the reconstruction, done by the Speech2Face model, resembles the true face images of the respective speakers.<br />
<br />
== Previous Work ==<br />
With visual and audio signals being so dominant and accessible in our daily life, there has been huge interest in how visual and audio perceptions interact with each other. Arandjelovic and Zisserman [1] leveraged the existing database of mp4 files to learn a generic audio representation to classify whether a video frame and an audio clip correspond to each other. These learned audio-visual representations have been used in a variety of setting, including cross-modal retrieval, sound source localization and sound source separation. This also paved the path for specifically studying the association between faces and voices of agents in the field of computer vision. In particular, cross-modal signals extracted from faces and voices have been proposed as a binary or multi-task classification task and there have been some promising results. Studies have been able to identify active speakers of a video, separate speech from multiple concurrent sources, predict lip motion from speech, and even learn the emotion of the agents based on their voices.<br />
<br />
Recently, various methods have been suggested to use various audio signals to reconstruct visual information, where the reconstructed subject is subjected to a priori. Notably, Duarte et al. [2] were able to synthesize the exact face images and expression of an agent from speech using a GAN model. This paper instead hopes to recover the dominant and generic facial structure from a speech.<br />
<br />
== Motivation ==<br />
It seems to be a common trait among humans to imagine what some people look like when we hear their voices before we have seen what they look lke. There is a strong connection between speech and appearance, which is a direct result of the factors that affect speech, including age, gender, and facial bone structure. In addition, other voice-appearance correlations stem from the way in which we talk: language, accent, speed, pronunciations, etc. These properties of speech are often common among many different nationalities and cultures, which can, in turn, translate to common physical features among different voices. Namely, from an input audio segment of a person speaking, the method would reconstruct an image of the person’s face in a canonical form (frontal-facing, neutral expression). The goal was to study to what extent people can infer how someone else looks from the way they talk. Rather than predicting a recognizable image of the exact face, the authors were more interested in capturing the dominant facial features.<br />
<br />
== Model Architecture == <br />
<br />
'''Speech2Face model and training pipeline'''<br />
<br />
[[File:ModelFramework.jpg|center]]<br />
<br />
<div style="text-align:center;"> Figure 1. '''Speech2Face model and training pipeline''' </div><br />
<br />
<br />
<br />
The Speech2Face Model used to achieve the desired result consist of 2 parts - a voice encoder which takes in a spectrogram of speech as input and outputs low dimensional face features, and a face decoder which takes in face features as input and outputs a normalized image of a face (neutral expression, looking forward). Figure 1 gives a visual representation of the pipeline of the entire model, from video input to a recognizable face. The face decoder itself was taken from previous work by Cole et al [3] and will not be explored in great detail here, but in essence the facenet model is combined with a single multilayer perceptron layer, the result of which is passed through a convolutional neural network to determine the texture of the image, and a multilayer perception to determine the landmark locations. The two results are combined to form an image. This model was trained using the VGG-Face model as input. It was also trained separately and remained fixed during the voice encoder training. The variability in facial expressions, head positions and lighting conditions of the face images creates a challenge to both the design and training of the Speech2Face model. To avoid this problem the model is trained to first regress to a low dimensional intermediate representation of the face. The VGG-Face model, a face recognition model that is pretrained on a largescale face database [5] is used to extract a 4069-D face feature from the penultimate layer of the network. <br />
<br />
'''Voice Encoder Architecture''' <br />
<br />
[[File:VoiceEncoderArch.JPG|center]]<br />
<br />
<div style="text-align:center;"> Table 1: '''Voice encoder architecture''' </div><br />
<br />
<br />
<br />
The voice encoder itself is a convolutional neural network, which transforms the input spectrogram into pseudo face features. The exact architecture is given in Table 1. The model alternates between convolution, ReLU, batch normalization layers, and layers of max-pooling. In each max-pooling layer, pooling is only done along the temporal dimension of the data. This is to ensure that the frequency, an important factor in determining vocal characteristics such as tone, is preserved. In the final pooling layer, an average pooling is applied along the temporal dimension. This allows the model to aggregate information over time and allows the model to be used for input speeches of varying lengths. Two fully connected layers at the end are used to return a 4096-dimensional facial feature output.<br />
<br />
'''Training'''<br />
<br />
The AVSSpeech dataset, a large-scale audio-visual dataset is used for the training. AVSSpeech dataset is comprised of millions of video segments from Youtube with over 100,000 different people. The training data is composed of educational videos and does not provide an accurate representation of the global population, which will clearly affect the model. Also note that facial features that are irrelevant to speech, like hair color, may be predicted by the model. From each video, a 224x224 pixels image of the face was passed through the face decoder to compute a facial feature vector. Combined with a spectrogram of the audio, a training and test set of 1.7 and 0.15 million entries respectively were constructed.<br />
<br />
The voice encoder is trained in a self-supervised manner. A frame that contains the face is extracted from each video and then inputted to the VGG-Face model to extract the feature vector <math>v_f</math>, the 4096-dimensional facial feature vector given by the face decoder on a single frame from the input video. This provides the supervision signal for the voice-encoder. The feature <math>v_s</math>, the 4096 dimensional facial feature vector from the voice encoder, is trained to predict <math>v_f</math>.<br />
<br />
In order to train this model, a proper loss function must be defined. The L1 norm of the difference between <math>v_s</math> and <math>v_f</math>, given by <math>||v_f - v_s||_1</math>, may seem like a suitable loss function, but in actuality results in unstable results and long training times. Figure 2, below, shows the difference in predicted facial features given by <math>||v_f - v_s||_1</math> and the following loss. Based on the work of Castrejon et al. [4], a loss function is used which penalizes the differences in the last layer of the face decoder <math>f_{VGG}</math> and the first layer <math>f_{dec}</math>. The final loss function is given by: $$L_{total} = ||f_{dec}(v_f) - f_{dec}(v_s)|| + \lambda_1||\frac{v_f}{||v_f||} - \frac{v_s}{||v_s||}||^2_2 + \lambda_2 L_{distill}(f_{VGG}(v_f), f_{VGG}(v_s))$$<br />
This loss penalizes on both the normalized Euclidean distance between the 2 facial feature vectors and the knowledge distillation loss, which is given by: $$L_{distill}(a,b) = -\sum_ip_{(i)}\text{log}p_{(i)}(b)$$ $$p_{(i)}(a) = \frac{\text{exp}(a_i/T)}{\sum_j \text{exp}(a_j/T)}$$ Knowledge distillation is used as an alternative to Cross-Entropy. By recommendation of Cole et al [3], <math> T = 2 </math> was used to ensure a smooth activation. <math>\lambda_1 = 0.025</math> and <math>\lambda_2 = 200</math> were chosen so that magnitude of the gradient of each term with respect to <math>v_s</math> are of similar scale at the <math>1000^{th}</math> iteration.<br />
<br />
<center><br />
[[File:L1vsTotalLoss.png | 700px]]<br />
</center><br />
<br />
<div style="text-align:center;"> Figure 2: '''Qualitative results on the AVSpeech test set''' </div><br />
<br />
== Results ==<br />
<br />
'''Confusion Matrix and Dataset statistics'''<br />
<br />
<center><br />
[[File:Confusionmatrix.png| 600px]]<br />
</center><br />
<br />
<div style="text-align:center;"> Figure 3. '''Facial attribute evaluation''' </div><br />
<br />
<br />
<br />
In order to determine the similarity between the generated images and the ground truth, a commercial service known as Face++ which classifies faces for distinct attributes (such as gender, ethnicity, etc) was used. Figure 3 gives a confusion matrix based on gender, ethnicity, and age. By examining these matrices, it is seen that the Speech2Face model performs very well on gender, only misclassifying 6% of the time. Similarly, the model performs fairly well on ethnicities, especially with white or Asian faces. Although the model performs worse on black and Indian faces, that can be attributed to the vastly unbalanced data, where 50% of the data represented a white face, and 80% represented a white or Asian face. <br />
<br />
'''Feature Similarity'''<br />
<br />
<center><br />
[[File:FeatSim.JPG]]<br />
</center><br />
<br />
<div style="text-align:center;"> Table 2. '''Feature similarity''' </div><br />
<br />
<br />
<br />
Another examination of the result is the similarity of features predicted by the Speech2Face model. The cosine, L1, and L2 distance between the facial feature vector produced by the model and the true facial feature vector from the face decoder were computed, and presented, above, in Table 2. A comparison of facial similarity was also done based on the length of audio input. From the table, it is evident that the 6-second audio produced a lower cosine, L1, and L2 distance, resulting in a facial feature vector that is closer to the ground truth. <br />
<br />
'''S2f -> Face retrieval performance'''<br />
<br />
<center><br />
[[File: Retrieval.JPG]]<br />
</center><br />
<br />
<div style="text-align:center;"> Table 3. '''S2F -> Face retrieval performance''' </div><br />
<br />
<br />
<br />
The performance of the model was also examined on how well it could produce the original image. The R@K metric, also known as retrieval performance by recall at K, was developed in which the K closest images in distance to the output of the model are found, and the chance that the original image is within those K images is the R@K score. A higher R@K score indicates better performance. From Table 3, above, we see that both the 3-second and 6-second audio showed significant improvement over random chance, with the 6-second audio performing slightly better.<br />
<br />
== Conclusion ==<br />
The report presented a novel study of face reconstruction from audio recordings of a person speaking. The model was demonstrated to be able to predict plausible face reconstructions with similar facial features to real images of the person speaking. The problem was addressed by learning to align the feature space of speech to that of a pretrained face decoder. The model was trained on millions of videos of people speaking from YouTube. The model was then evaluated by comparing the reconstructed faces with a commercial facial detection service. The authors believe that facial reconstruction allows a more comprehensive view of voice-face correlation compared to predicting individual features, which may lead to new research opportunities and applications.<br />
<br />
== Discussion and Critiques ==<br />
<br />
There is evidence that the results of the model may be heavily influenced by external factors:<br />
<br />
1. Their method of sampling random YouTube videos resulted in an unbalanced sample in terms of ethnicity. Over half of the samples were white. We also saw a large bias in the model's prediction of ethnicity towards white. The bias in the results shows that the model may be overfitting the training data and puts into question what the performance of the model would be when trained and tested on a balanced dataset. <br />
<br />
2. The model was shown to infer different face features based on language. This puts into question how heavily the model depends on the spoken language. The paper mentioned the quality of face reconstruction may be affected by uncommon languages, where English is the most popular language on Youtube(training set). Testing a more controlled sample where all speech recording was of the same language may help address this concern to determine the model's reliance on spoken language.<br />
<br />
3. The evaluation of the result is also highly dependent on the Face++ classifiers. Since they compare the age, gender, and ethnicity by running the Face++ classifiers on the original images and the reconstructions to evaluate their model, the model that they create can only be as good as the one they are using to evaluate it. Therefore, any limitations of the Face++ classifier may become a limitation of Speech2Face and may result in a compounding effect on the miss-classification rate. <br />
<br />
4. Figure 4.b shows the AVSpeech dataset statistics. However, it doesn't show the statistics about speakers' ethnicity and the language of the video. If we train the model with a more comprehensive dataset that includes enough Asian/Indian English speakers and native language speakers will this increase the accuracy?<br />
<br />
5. One concern about the source of the training data, i.e. the Youtube videos, is that resolution varies a lot since the videos are randomly selected. That may be the reason why the proposed model performs badly on some certain features. For example, it is hard to tell the age when the resolution is bad because the wrinkles on the face are neglected.<br />
<br />
6. The topic of this project is very interesting, but I highly doubt this model will be practical in real world problems. Because there are many factors to affect a person's sound in the real world environment. Sounds such like phone clock, TV, car horn and so on. These sounds will decrease the accuracy of the predicted result of the model.<br />
<br />
7. A lot of information can be obtained from someone's voice, this can potentially be useful for detective work and crime scene investigation. In our world of increasing surveillance, public voice recording is quite common and we can reconstruct images of potential suspects based on their voice. In order for this to be achieved, the model has to be thoroughly trained and tested to avoid false positives as it could have highly destructive outcome for a falsely convicted suspect.<br />
<br />
8. This is a very interesting topic, and this summary has a good structure for readers. Since this model uses Youtube to train model, but I think one problem is that most of the YouTubers are adult, and there are many additional reasons that make this dataset be highly unbalanced. What is more, some people may have a baby voice, this also could affect the performance of the model. But overall, this is a meaningful topic, it might help police to locate the suspects. So it might be interesting to apply this to the police.<br />
<br />
9. In addition, it seems very unlikely that any results coming from this model would ever be held in a regard even remotely close to being admissible in court to identify a person of interest until the results are improved and the model can be shown to work in real world applications. Otherwise, there seems to be very little use for such technology and it could have negative impacts on people if they were to be depicted in an unflattering way by the model based on their voice.<br />
<br />
== References ==<br />
[1] R. Arandjelovic and A. Zisserman. Look, listen and learn. In<br />
IEEE International Conference on Computer Vision (ICCV),<br />
2017.<br />
<br />
[2] A. Duarte, F. Roldan, M. Tubau, J. Escur, S. Pascual, A. Salvador, E. Mohedano, K. McGuinness, J. Torres, and X. Giroi-Nieto. Wav2Pix: speech-conditioned face generation using generative adversarial networks. In IEEE International<br />
Conference on Acoustics, Speech and Signal Processing<br />
(ICASSP), 2019.<br />
<br />
[3] F. Cole, D. Belanger, D. Krishnan, A. Sarna, I. Mosseri, and W. T. Freeman. Synthesizing normalized faces from facial identity features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.<br />
<br />
[4] L. Castrejon, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba. Learning aligned cross-modal representations from weakly aligned data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.<br />
<br />
[5] O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In British Machine Vision Conference (BMVC), 2015.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining&diff=48512Describtion of Text Mining2020-11-30T20:08:17Z<p>Jdhchow: </p>
<hr />
<div>== Presented by == <br />
Yawen Wang, Danmeng Cui, Zijie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid<br />
<br />
== Introduction ==<br />
This paper focuses on the different text mining techniques and the applications of text mining in the healthcare and biomedical domain. The text mining field has been popular as a result of the amount of text data that is available in different forms. The text data is bound to grow even more in 2020, indicating a 50 times growth since 2010. Text is a kind of unstructured information, which is easy for humans to construct and understand, but it is difficult for machines. Hence, there is a need to design algorithms to effectively process this avalanche of text. To further explore the text mining field, the related text mining approaches can be considered. The different text mining approaches relate to two main methods: knowledge delivery and traditional data mining methods. <br />
<br />
The authors note that knowledge delivery methods involve the application of different steps to a specific data set to create specific patterns. Research in knowledge delivery methods has evolved over the years due to advances in hardware and software technology. On the other hand, data mining has experienced substantial development through the intersection of three fields: databases, machine learning, and statistics. As brought out by the authors, text mining approaches focus on the exploration of information from a specific text. The information explored is in the form of structured, semi-structured, and unstructured text. It is important to note that text mining covers different sets of algorithms and topics that include information retrieval. The topics and algorithms are used for analyzing different text forms.<br />
<br />
== Preprocessing ==<br />
The authors introduce multiple methods of preprocessing text.<br />
<br />
'''1. Tokenization'''<br />
<br />
This process split text into units of words of phrases known as tokens while removing unnecessary characters.<br />
<br />
'''2. Filtering'''<br />
<br />
Filtering is a process by which unnecessary words or characters are removed. Often these include punctuation, prepositions, and conjugations. The resulting corpus then contains words with maximal importance in distinguishing between classes.<br />
<br />
'''3. Stemming'''<br />
<br />
Stemming extracts the roots of words. It is a language dependent process.<br />
<br />
== Text Representation and Encoding ==<br />
In this section of the paper, the authors explore the different ways in which the text can be represented on a large collection of documents. One common way of representing the documents is in the form of a bag of words. The bag of words considers the occurrences of different terms. In different text mining applications, documents are ranked and represented as vectors so as to display the significance of any word. The authors note that the three basic models used are vector space, inference network, and the probabilistic models. The vector space model is used to represent documents by converting them into vectors. In the model, a variable is used to represent each model to indicate the importance of the word in the document. The words are weighted using the TF-IDF scheme computed as <br />
<br />
$$<br />
q(w)=f_d(w)*log{\frac{|D|}{f_D(w)}}<br />
$$<br />
<br />
In many text mining algorithms, one of the key components is preprocessing. Preprocessing consists of different tasks that include filtering, tokenization, stemming, and lemmatization. The first step is tokenization, where a character sequence is broken down into different words or phrases. After the breakdown, filtering is carried out to remove some words. The various word inflected forms are grouped together through lemmatization, and later, the derived roots of the derived words are obtained through stemming.<br />
<br />
== Classification ==<br />
Classification in Text Mining aims to assigned predefined classes to text documents. For a set <math>\mathcal{D} = {d_1, d_2, ... d_n}</math> of documents, such that each <math>d_i</math> is mapped to a label <math>l_i</math> from the set <math>\mathcal{L} = {l_1, l_2, ... l_k}</math>. The goal is to find a classification model <math>f</math> such that: <math>\\</math><br />
$$<br />
f: \mathcal{D} \rightarrow \mathcal{L} \quad \quad \quad f(\mathcal{d}) = \mathcal{l}<br />
$$<br />
The author illustrates 4 different classifiers that are commonly used in text mining.<br />
<br />
<br />
'''1. Naive Bayes Classifier''' <br />
<br />
Bayes rule is used to classify new examples and select the class that has the generated result that occurs most often. <br />
Naive Bayes Classifier models the distribution of documents in each class using a probabilistic model assuming that the distribution<br />
of different terms is independent of each other. The models commonly used in this classifier tried to find the posterior probability of a class based on the distribution and assumes that the documents generated are based on a mixture model parameterized by <math>\theta</math> and compute the likelihood of a document using the sum of probabilities over all mixture component. In addition, the Naive Bayes Classifier can help get around the curse of dimensionality, which may arise with high-dimensional data, such as text. <br />
<br />
'''2. Nearest Neighbour Classifier'''<br />
<br />
Nearest Neighbour Classifier uses distance-based measures to perform the classification. The documents which belong to the same class are more likely "similar" or close to each other based on the similarity measure. The classification of the test documents is inferred from the class labels of similar documents in the training set.<br />
<br />
'''3. Decision Tree Classifier'''<br />
<br />
A hierarchical tree of the training instances, in which a condition on the attribute value is used to divide the data hierarchically. The decision tree recursively partitions the training data set into smaller subdivisions based on a set of tests defined at each node or branch. Each node of the tree is a test of some attribute of the training instance, and each branch descending from the node corresponds to one of the values of this attribute. The conditions on the nodes are commonly defined by the terms in the text documents.<br />
<br />
'''4. Support Vector Machines'''<br />
<br />
SVM is a form of Linear Classifiers which are models that makes a classification decision based on the value of the linear combinations of the documents features. The output of a linear predictor is defined to the <math> y=\vec{a} \cdot \vec{x} + b</math> where <math>\vec{x}</math> is the normalized document word frequency vector, <math>\vec{a}</math> is a vector of coefficient and <math>b</math> is a scalar. Support Vector Machines attempts to find a linear separators between various classes. An advantage of the SVM method is it is robust to high dimensionality.<br />
<br />
== Clustering ==<br />
Clustering has been extensively studied in the context of the text as it has a wide range of applications such as visualization and document organization.<br />
<br />
Clustering algorithms are used to group similar documents and thus aids in information retrieval. Text clustering can be in different levels of granularities, where clusters can be documents, paragraphs, sentences, or terms. Since text data has numerous distance characteristics that demand the design of text-specific algorithms for the task, using a binary vector to represent the text document is simply not enough. Here are some unique properties of text representation:<br />
<br />
1. Text representation has a large dimensionality, in which the size of the vocabulary from which the documents are drawn is massive, but a document might only contain a small number of words.<br />
<br />
2. The words in the documents are usually correlated with each other. Need to take the correlation into consideration when designing algorithms.<br />
<br />
3. The number of words differs from one another of the document. Thus the document needs to be normalized first before the clustering process.<br />
<br />
There are 3 most commonly used text clustering algorithms presented.<br />
<br />
<br />
'''1. Hierarchical Clustering algorithms''' <br />
<br />
Hierarchical Clustering algorithms build d a group of clusters that can be depicted as a hierarchy of clusters. The hierarchy can be constructed in top-down (divisive) or bottom-up (agglomeration). Hierarchical clustering algorithms are one of the Distanced-based clustering algorithms, i.e., using a similarity function to measure the closeness between text documents.<br />
<br />
In the top-down approach, the algorithm begins with one cluster which includes all the documents. we recursively split this cluster into sub-clusters.<br />
Here is an example of a Hierarchical Clustering algorithm, the data is to be clustered by the euclidean distance. This method builds the hierarchy from the individual elements by progressively merging clusters. In our example, we have six elements {a} {b} {c} {d} {e} and {f}. The first step determines which elements to merge in a cluster by taking the two closest elements, according to the chosen distance.<br />
<br />
<br />
[[File:418px-Hierarchical clustering simple diagram.svg.png| 300px | center]]<br />
<br />
<br />
<div align="center">Figure 1: Hierarchical Clustering Raw Data</div><br />
<br />
<br />
<br />
[[File:250px-Clusters.svg (1).png| 200px | center]]<br />
<br />
<br />
<div align="center">Figure 2: Hierarchical Clustering Clustered Data</div><br />
<br />
A main advantage of hierarchical clustering is that the algorithm only needs to be done once for any number of clusters (ie. if an individual wishes to use a different number of clusters than originally intended, they do not need to repeat the algorithm)<br />
<br />
'''2. k-means Clustering'''<br />
<br />
k-means clustering is a partitioning algorithm that partitions n documents in the context of text data into k clusters.<br />
<br />
Input: Document D, similarity measure S, number k of cluster<br />
Output: Set of k clusters<br />
Select randomly ''k'' datapoints as starting centroids<br />
While ''not converged'' do <br />
Assign documents to the centroids based on the closest similarity<br />
Calculate the cluster centroids for all clusters<br />
return ''k clusters''<br />
<br />
The main disadvantage of k-means clustering is that it is indeed very sensitive to the initial choice of the number of k. Also, since the function is ran until clusters converges, k-means clustering tends to take longer to perform than hierarchical clustering. On the other hand, advantages of k-means clustering are that it is simple to implement, the algorithm scales well to large datasets, and the results are easily interpretable.<br />
<br />
<br />
'''3. Probabilistic Clustering and Topic Models'''<br />
<br />
Topic modeling is one of the most popular probabilistic clustering algorithms in recent studies. The main idea is to create a *probabilistic generative model* for the corpus of text documents. In topic models, documents are a mixture of topics, where each topic represents a probability distribution over words.<br />
<br />
There are two main topic models:<br />
* Probabilistic Latent Semantic Analysis (pLSA)<br />
* Latent Dirichlet Allocation (LDA)<br />
<br />
The paper covers LDA in more detail. LDA is a state-of-the-art unsupervised algorithm for extracting topics from a collection of documents.<br />
<br />
Given <math>\mathcal{D} = \{d_1, d_2, \cdots, d_{|\mathcal{D}|}\}</math> is the corpus and <math>\mathcal{V} = \{w_1, w_2, \cdots, w_{|\mathcal{V}|}\}</math> is the vocabulary of the corpus. <br />
<br />
A topic is <math>z_j, 1 \leq j \leq K</math> is a multinomial probability distribution over <math>|\mathcal{V}|</math> words. <br />
<br />
The distribution of word given document is:<br />
<br />
<math>p(w_i|d) = \Sigma_{j=1}^K p(w_i|z_j)p(z_j|d)</math><br />
<br />
The LDA assumes the following generative process for the corpus of <math>\mathcal{D}</math><br />
* For each topic <math>k\in \{1,2,\cdots, K\}</math>, sample a word distribution <math>\phi_k \sim Dir(\beta)</math><br />
* For each document <math>d \in \{1,2,\cdots,D\}</math><br />
** Sample a topic distribution <math>\theta_d \sim Dir(\alpha)</math><br />
** For each word <math>w_n, n \in \{1,2,\cdots,N\}</math> in document <math>d</math><br />
*** Sample a topic <math>z_i \sim Mult(\theta_d)</math><br />
*** Sample a word <math>w_n \sim Mult(\phi_{z_i})</math><br />
<br />
In practice, LDA is often used as a module in more complicated models and has already been applied to a wide variety of domains. In addition, many variations of LDA has been created, including supervised LDA (sLDA) and hierarchical LDA (hLDA)<br />
<br />
== Information Extraction ==<br />
Information Extraction (IE) is the process of extracting useful, structured information from unstructured or semi-structured text. It automatically extracts based on our command. <br />
<br />
For example, consider the following sentence, “XYZ company was founded by Peter in the year of 1950”<br />
We can identify the following information:<br />
<br />
Founderof(Peter, XYZ)<br />
Foundedin(1950, XYZ)<br />
<br />
The author mentioned 4 parts that are important for Information Extraction<br />
<br />
'''1. Namely Entity Recognition(NER)'''<br />
<br />
This is the process of identifying real-world entity from free text, such as "Apple Inc.", "Donald Trump", "PlayStation 5" etc. Moreover, the task is to identify the category of these entities, such as "Apple Inc." is in the category of the company, "Donald Trump" is in the category of the USA president, and "PlayStation 5" is in the category of the entertainment system. <br />
<br />
'''2. Hidden Markov Model'''<br />
<br />
Since traditional probabilistic classification does not consider the predicted labels of neighbor words, we use the Hidden Markov Model when doing Information Extraction. This model is different because it considers the label of one word depends on the previous words that appeared. <br />
<br />
'''3. Conditional Random Fields'''<br />
<br />
This is a technique that is widely used in Information Extraction. The definition of it is related to graph theory. <br />
let G = (V, E) be a graph and Yv stands for the index of the vertices in G. Then (X, Y) is a conditional random field, when the random variables Yv, conditioned on X, obey Markov property with respect to the graph, and:<br />
p(Yv |X, Yw ,w , v) = p(Yv |X, Yw ,w ∼ v), where w ∼ v means w and v are neighbors in G.<br />
<br />
'''4. Relation Extraction'''<br />
<br />
This is a task of finding semantic relationships between word entities in text documents. Such as "Seth Curry" is the brother of "Stephen Curry", if there is a document including these two names, the task is to identify the relationship of these two entities.<br />
<br />
== Biomedical Application ==<br />
<br />
Text mining has several applications in the domain of biomedical sciences. The explosion of academic literature in the field has made it quite hard for scientists to keep up with novel research. This is why text mining techniques are ever so important in making the knowledge digestible.<br />
<br />
The text mining techniques are able to extract meaningful information from large data by making use of biomedical ontology, which is a compilation of a common set of terms used in an area of knowledge. The Unified Medical Language System (UMLS) is the most comprehensive such resource, consisting of definitions of biomedical jargon. Several information extraction algorithms rely on the ontology to perform tasks such as Named Entity Recognition (NER) and Relation Extraction.<br />
<br />
NER involves locating and classifying biomedical entities into meaningful categories and assigning semantic representation to those entities. The NER methods can be broadly grouped into Dictionary-based, Rule-based and Statistical approaches. Relation extraction, on the other hand, is the process of determining relationships between the entities. This is accomplished mainly by identifying the correlation between entities through analyzing the frequency of terms, as well as rules defined by domain experts. Moreover, modern algorithms are also able to summarize large documents and answer natural language questions posed by humans.<br />
<br />
== Conclusion ==<br />
<br />
This paper gave a holistic overview of the methods and applications of text mining, particularly its relevance in the biomedical domain. It highlights several popular algorithms and summarizes them along with their advantages, limitations and some potential situations where they could be used. Because of ever-growing data, for example, the very high volume of scientific literature being produced every year, the interest in this field is massive and is bound to grow in the future.<br />
<br />
== Critiques==<br />
<br />
This is a very detailed approach to introduce some different algorithms on text mining. Since many algorithms are given, it might be a good idea to compare their performances on text mining by training them on some text data and compare them to the former baselines, to see if there exists any improvement.<br />
<br />
it is a detailed summary of the techniques used in text mining. It would be more helpful if some dataset can be included for training and testing. The algorithms were grouped by different topics so that different datasets and measurements are required.<br />
<br />
It would be better for the paper to include test accuracy for testing and training sets to support text mining is a more efficient and effective algorithm compared to other techniques. Moreover, this paper mentioned Text Mining approach can be used to extract high-quality information from videos. It is to believe that extracting from videos is much more difficult than images and texts. How is it possible to retain its test accuracy at a good level for videos?<br />
<br />
Preprocessing an important step to analyze text, so it might be better to have the more details about that. For example, what types of words are usually removed and show we record the relative position of each word in the sentence. If one close related sentences were split into two sentences, how can we capture their relations?<br />
<br />
== References ==<br />
<br />
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering, and extraction techniques. arXiv preprint arXiv:1707.02919.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining&diff=48511Describtion of Text Mining2020-11-30T20:08:07Z<p>Jdhchow: </p>
<hr />
<div>== Presented by == <br />
Yawen Wang, Danmeng Cui, Zijie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid<br />
<br />
== Introduction ==<br />
This paper focuses on the different text mining techniques and the applications of text mining in the healthcare and biomedical domain. The text mining field has been popular as a result of the amount of text data that is available in different forms. The text data is bound to grow even more in 2020, indicating a 50 times growth since 2010. Text is a kind of unstructured information, which is easy for humans to construct and understand, but it is difficult for machines. Hence, there is a need to design algorithms to effectively process this avalanche of text. To further explore the text mining field, the related text mining approaches can be considered. The different text mining approaches relate to two main methods: knowledge delivery and traditional data mining methods. <br />
<br />
The authors note that knowledge delivery methods involve the application of different steps to a specific data set to create specific patterns. Research in knowledge delivery methods has evolved over the years due to advances in hardware and software technology. On the other hand, data mining has experienced substantial development through the intersection of three fields: databases, machine learning, and statistics. As brought out by the authors, text mining approaches focus on the exploration of information from a specific text. The information explored is in the form of structured, semi-structured, and unstructured text. It is important to note that text mining covers different sets of algorithms and topics that include information retrieval. The topics and algorithms are used for analyzing different text forms.<br />
<br />
== Preprocessing ==<br />
The authors introduce multiple methods of preprocessing text.<br />
<br />
'''1. Tokenization'''<br />
<br />
This process split text into units of words of phrases known as tokens while removing unnecessary characters.<br />
<br />
'''2. Filtering'''<br />
<br />
Filtering is a process by which unnecessary words or characters are removed. Often these include punctuation, prepositions, and conjugations. The resulting corpus then contains words with maximal importance in distinguishing between classes.<br />
<br />
'''3. Stemming''''<br />
<br />
Stemming extracts the roots of words. It is a language dependent process.<br />
<br />
== Text Representation and Encoding ==<br />
In this section of the paper, the authors explore the different ways in which the text can be represented on a large collection of documents. One common way of representing the documents is in the form of a bag of words. The bag of words considers the occurrences of different terms. In different text mining applications, documents are ranked and represented as vectors so as to display the significance of any word. The authors note that the three basic models used are vector space, inference network, and the probabilistic models. The vector space model is used to represent documents by converting them into vectors. In the model, a variable is used to represent each model to indicate the importance of the word in the document. The words are weighted using the TF-IDF scheme computed as <br />
<br />
$$<br />
q(w)=f_d(w)*log{\frac{|D|}{f_D(w)}}<br />
$$<br />
<br />
In many text mining algorithms, one of the key components is preprocessing. Preprocessing consists of different tasks that include filtering, tokenization, stemming, and lemmatization. The first step is tokenization, where a character sequence is broken down into different words or phrases. After the breakdown, filtering is carried out to remove some words. The various word inflected forms are grouped together through lemmatization, and later, the derived roots of the derived words are obtained through stemming.<br />
<br />
== Classification ==<br />
Classification in Text Mining aims to assigned predefined classes to text documents. For a set <math>\mathcal{D} = {d_1, d_2, ... d_n}</math> of documents, such that each <math>d_i</math> is mapped to a label <math>l_i</math> from the set <math>\mathcal{L} = {l_1, l_2, ... l_k}</math>. The goal is to find a classification model <math>f</math> such that: <math>\\</math><br />
$$<br />
f: \mathcal{D} \rightarrow \mathcal{L} \quad \quad \quad f(\mathcal{d}) = \mathcal{l}<br />
$$<br />
The author illustrates 4 different classifiers that are commonly used in text mining.<br />
<br />
<br />
'''1. Naive Bayes Classifier''' <br />
<br />
Bayes rule is used to classify new examples and select the class that has the generated result that occurs most often. <br />
Naive Bayes Classifier models the distribution of documents in each class using a probabilistic model assuming that the distribution<br />
of different terms is independent of each other. The models commonly used in this classifier tried to find the posterior probability of a class based on the distribution and assumes that the documents generated are based on a mixture model parameterized by <math>\theta</math> and compute the likelihood of a document using the sum of probabilities over all mixture component. In addition, the Naive Bayes Classifier can help get around the curse of dimensionality, which may arise with high-dimensional data, such as text. <br />
<br />
'''2. Nearest Neighbour Classifier'''<br />
<br />
Nearest Neighbour Classifier uses distance-based measures to perform the classification. The documents which belong to the same class are more likely "similar" or close to each other based on the similarity measure. The classification of the test documents is inferred from the class labels of similar documents in the training set.<br />
<br />
'''3. Decision Tree Classifier'''<br />
<br />
A hierarchical tree of the training instances, in which a condition on the attribute value is used to divide the data hierarchically. The decision tree recursively partitions the training data set into smaller subdivisions based on a set of tests defined at each node or branch. Each node of the tree is a test of some attribute of the training instance, and each branch descending from the node corresponds to one of the values of this attribute. The conditions on the nodes are commonly defined by the terms in the text documents.<br />
<br />
'''4. Support Vector Machines'''<br />
<br />
SVM is a form of Linear Classifiers which are models that makes a classification decision based on the value of the linear combinations of the documents features. The output of a linear predictor is defined to the <math> y=\vec{a} \cdot \vec{x} + b</math> where <math>\vec{x}</math> is the normalized document word frequency vector, <math>\vec{a}</math> is a vector of coefficient and <math>b</math> is a scalar. Support Vector Machines attempts to find a linear separators between various classes. An advantage of the SVM method is it is robust to high dimensionality.<br />
<br />
== Clustering ==<br />
Clustering has been extensively studied in the context of the text as it has a wide range of applications such as visualization and document organization.<br />
<br />
Clustering algorithms are used to group similar documents and thus aids in information retrieval. Text clustering can be in different levels of granularities, where clusters can be documents, paragraphs, sentences, or terms. Since text data has numerous distance characteristics that demand the design of text-specific algorithms for the task, using a binary vector to represent the text document is simply not enough. Here are some unique properties of text representation:<br />
<br />
1. Text representation has a large dimensionality, in which the size of the vocabulary from which the documents are drawn is massive, but a document might only contain a small number of words.<br />
<br />
2. The words in the documents are usually correlated with each other. Need to take the correlation into consideration when designing algorithms.<br />
<br />
3. The number of words differs from one another of the document. Thus the document needs to be normalized first before the clustering process.<br />
<br />
There are 3 most commonly used text clustering algorithms presented.<br />
<br />
<br />
'''1. Hierarchical Clustering algorithms''' <br />
<br />
Hierarchical Clustering algorithms build d a group of clusters that can be depicted as a hierarchy of clusters. The hierarchy can be constructed in top-down (divisive) or bottom-up (agglomeration). Hierarchical clustering algorithms are one of the Distanced-based clustering algorithms, i.e., using a similarity function to measure the closeness between text documents.<br />
<br />
In the top-down approach, the algorithm begins with one cluster which includes all the documents. we recursively split this cluster into sub-clusters.<br />
Here is an example of a Hierarchical Clustering algorithm, the data is to be clustered by the euclidean distance. This method builds the hierarchy from the individual elements by progressively merging clusters. In our example, we have six elements {a} {b} {c} {d} {e} and {f}. The first step determines which elements to merge in a cluster by taking the two closest elements, according to the chosen distance.<br />
<br />
<br />
[[File:418px-Hierarchical clustering simple diagram.svg.png| 300px | center]]<br />
<br />
<br />
<div align="center">Figure 1: Hierarchical Clustering Raw Data</div><br />
<br />
<br />
<br />
[[File:250px-Clusters.svg (1).png| 200px | center]]<br />
<br />
<br />
<div align="center">Figure 2: Hierarchical Clustering Clustered Data</div><br />
<br />
A main advantage of hierarchical clustering is that the algorithm only needs to be done once for any number of clusters (ie. if an individual wishes to use a different number of clusters than originally intended, they do not need to repeat the algorithm)<br />
<br />
'''2. k-means Clustering'''<br />
<br />
k-means clustering is a partitioning algorithm that partitions n documents in the context of text data into k clusters.<br />
<br />
Input: Document D, similarity measure S, number k of cluster<br />
Output: Set of k clusters<br />
Select randomly ''k'' datapoints as starting centroids<br />
While ''not converged'' do <br />
Assign documents to the centroids based on the closest similarity<br />
Calculate the cluster centroids for all clusters<br />
return ''k clusters''<br />
<br />
The main disadvantage of k-means clustering is that it is indeed very sensitive to the initial choice of the number of k. Also, since the function is ran until clusters converges, k-means clustering tends to take longer to perform than hierarchical clustering. On the other hand, advantages of k-means clustering are that it is simple to implement, the algorithm scales well to large datasets, and the results are easily interpretable.<br />
<br />
<br />
'''3. Probabilistic Clustering and Topic Models'''<br />
<br />
Topic modeling is one of the most popular probabilistic clustering algorithms in recent studies. The main idea is to create a *probabilistic generative model* for the corpus of text documents. In topic models, documents are a mixture of topics, where each topic represents a probability distribution over words.<br />
<br />
There are two main topic models:<br />
* Probabilistic Latent Semantic Analysis (pLSA)<br />
* Latent Dirichlet Allocation (LDA)<br />
<br />
The paper covers LDA in more detail. LDA is a state-of-the-art unsupervised algorithm for extracting topics from a collection of documents.<br />
<br />
Given <math>\mathcal{D} = \{d_1, d_2, \cdots, d_{|\mathcal{D}|}\}</math> is the corpus and <math>\mathcal{V} = \{w_1, w_2, \cdots, w_{|\mathcal{V}|}\}</math> is the vocabulary of the corpus. <br />
<br />
A topic is <math>z_j, 1 \leq j \leq K</math> is a multinomial probability distribution over <math>|\mathcal{V}|</math> words. <br />
<br />
The distribution of word given document is:<br />
<br />
<math>p(w_i|d) = \Sigma_{j=1}^K p(w_i|z_j)p(z_j|d)</math><br />
<br />
The LDA assumes the following generative process for the corpus of <math>\mathcal{D}</math><br />
* For each topic <math>k\in \{1,2,\cdots, K\}</math>, sample a word distribution <math>\phi_k \sim Dir(\beta)</math><br />
* For each document <math>d \in \{1,2,\cdots,D\}</math><br />
** Sample a topic distribution <math>\theta_d \sim Dir(\alpha)</math><br />
** For each word <math>w_n, n \in \{1,2,\cdots,N\}</math> in document <math>d</math><br />
*** Sample a topic <math>z_i \sim Mult(\theta_d)</math><br />
*** Sample a word <math>w_n \sim Mult(\phi_{z_i})</math><br />
<br />
In practice, LDA is often used as a module in more complicated models and has already been applied to a wide variety of domains. In addition, many variations of LDA has been created, including supervised LDA (sLDA) and hierarchical LDA (hLDA)<br />
<br />
== Information Extraction ==<br />
Information Extraction (IE) is the process of extracting useful, structured information from unstructured or semi-structured text. It automatically extracts based on our command. <br />
<br />
For example, consider the following sentence, “XYZ company was founded by Peter in the year of 1950”<br />
We can identify the following information:<br />
<br />
Founderof(Peter, XYZ)<br />
Foundedin(1950, XYZ)<br />
<br />
The author mentioned 4 parts that are important for Information Extraction<br />
<br />
'''1. Namely Entity Recognition(NER)'''<br />
<br />
This is the process of identifying real-world entity from free text, such as "Apple Inc.", "Donald Trump", "PlayStation 5" etc. Moreover, the task is to identify the category of these entities, such as "Apple Inc." is in the category of the company, "Donald Trump" is in the category of the USA president, and "PlayStation 5" is in the category of the entertainment system. <br />
<br />
'''2. Hidden Markov Model'''<br />
<br />
Since traditional probabilistic classification does not consider the predicted labels of neighbor words, we use the Hidden Markov Model when doing Information Extraction. This model is different because it considers the label of one word depends on the previous words that appeared. <br />
<br />
'''3. Conditional Random Fields'''<br />
<br />
This is a technique that is widely used in Information Extraction. The definition of it is related to graph theory. <br />
let G = (V, E) be a graph and Yv stands for the index of the vertices in G. Then (X, Y) is a conditional random field, when the random variables Yv, conditioned on X, obey Markov property with respect to the graph, and:<br />
p(Yv |X, Yw ,w , v) = p(Yv |X, Yw ,w ∼ v), where w ∼ v means w and v are neighbors in G.<br />
<br />
'''4. Relation Extraction'''<br />
<br />
This is a task of finding semantic relationships between word entities in text documents. Such as "Seth Curry" is the brother of "Stephen Curry", if there is a document including these two names, the task is to identify the relationship of these two entities.<br />
<br />
== Biomedical Application ==<br />
<br />
Text mining has several applications in the domain of biomedical sciences. The explosion of academic literature in the field has made it quite hard for scientists to keep up with novel research. This is why text mining techniques are ever so important in making the knowledge digestible.<br />
<br />
The text mining techniques are able to extract meaningful information from large data by making use of biomedical ontology, which is a compilation of a common set of terms used in an area of knowledge. The Unified Medical Language System (UMLS) is the most comprehensive such resource, consisting of definitions of biomedical jargon. Several information extraction algorithms rely on the ontology to perform tasks such as Named Entity Recognition (NER) and Relation Extraction.<br />
<br />
NER involves locating and classifying biomedical entities into meaningful categories and assigning semantic representation to those entities. The NER methods can be broadly grouped into Dictionary-based, Rule-based and Statistical approaches. Relation extraction, on the other hand, is the process of determining relationships between the entities. This is accomplished mainly by identifying the correlation between entities through analyzing the frequency of terms, as well as rules defined by domain experts. Moreover, modern algorithms are also able to summarize large documents and answer natural language questions posed by humans.<br />
<br />
== Conclusion ==<br />
<br />
This paper gave a holistic overview of the methods and applications of text mining, particularly its relevance in the biomedical domain. It highlights several popular algorithms and summarizes them along with their advantages, limitations and some potential situations where they could be used. Because of ever-growing data, for example, the very high volume of scientific literature being produced every year, the interest in this field is massive and is bound to grow in the future.<br />
<br />
== Critiques==<br />
<br />
This is a very detailed approach to introduce some different algorithms on text mining. Since many algorithms are given, it might be a good idea to compare their performances on text mining by training them on some text data and compare them to the former baselines, to see if there exists any improvement.<br />
<br />
it is a detailed summary of the techniques used in text mining. It would be more helpful if some dataset can be included for training and testing. The algorithms were grouped by different topics so that different datasets and measurements are required.<br />
<br />
It would be better for the paper to include test accuracy for testing and training sets to support text mining is a more efficient and effective algorithm compared to other techniques. Moreover, this paper mentioned Text Mining approach can be used to extract high-quality information from videos. It is to believe that extracting from videos is much more difficult than images and texts. How is it possible to retain its test accuracy at a good level for videos?<br />
<br />
Preprocessing an important step to analyze text, so it might be better to have the more details about that. For example, what types of words are usually removed and show we record the relative position of each word in the sentence. If one close related sentences were split into two sentences, how can we capture their relations?<br />
<br />
== References ==<br />
<br />
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering, and extraction techniques. arXiv preprint arXiv:1707.02919.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Improving_neural_networks_by_preventing_co-adaption_of_feature_detectors&diff=48503Improving neural networks by preventing co-adaption of feature detectors2020-11-30T19:45:42Z<p>Jdhchow: </p>
<hr />
<div>== Presented by ==<br />
Stan Lee, Seokho Lim, Kyle Jung, Dae Hyun Kim<br />
<br />
= Introduction =<br />
In this paper, Hinton et al. introduces a novel way to improve neural networks’ performance. By omitting neurons in hidden layers with a probability of 0.5, each hidden unit is prevented from relying on other hidden units being present during training. Hence there are fewer co-adaptations among them on the training data. Called “dropout,” this process is also an efficient alternative to training many separate networks and average their predictions on the test set.<br />
They used the standard, stochastic gradient descent algorithm and separated training data into mini-batches. An upper bound was set on the L2 norm of incoming weight vector for each hidden neuron, which was normalized if its size exceeds the bound. They found that using a constraint, instead of a penalty, forced model to do a more thorough search of the weight-space, when coupled with the very large learning rate that decays during training. <br />
Their dropout models included all of the hidden neurons, and their outgoing weights were halved to account for the chances of omission. The models were shown to result in lower test error rates on several datasets: MNIST; TIMIT; Reuters Corpus Volume; CIFAR-10; and ImageNet.<br />
<br />
= MNIST =<br />
The MNIST dataset contains 70,000 digit images of size 28 x 28. To see the impact of dropout, they used 4 different neural networks (784-800-800-10, 784-1200-1200-10, 784-2000-2000-10, 784-1200-1200-1200-10), using the same dropout rates as 50% for hidden neurons and 20% for visible neurons. Stochastic gradient descent was used with mini-batches of size 100 and a cross-entropy objective function as the loss function. Weights were updated after each minibatch, and training was done for 3000 epochs. An exponentially decaying learning rate <math>\epsilon</math> was used, with the initial value set as 10.0, and it was multiplied by 0.998 at the end of each epoch. At each hidden layer, the incoming weight vector for each hidden neuron was set an upper bound of its length, <math>l</math>, and they found from cross-validation that the results were the best when <math>l</math> = 15. Initial weights values were pooled from a normal distribution with mean 0 and standard deviation of 0.01. To update weights, an additional variable, ''p'', called momentum, was used to accelerate learning. The initial value of <math>p</math> was 0.5, and it increased linearly to the final value 0.99 during the first 500 epochs, remaining unchanged after. Also, when updating weights, the learning rate was multiplied by <math>1 – p</math>. <math>L</math> denotes the gradient of loss function.<br />
<br />
[[File:weights_mnist2.png|center|400px]]<br />
<br />
The best published result for a standard feedforward neural network was 160 errors. This was reduced to about 130 errors with 0.5 dropout and different L2 constraints for each hidden unit input weight. By omitting a random 20% of the input pixels in addition to the aforementioned changes, the number of errors was further reduced to 110. The following figure visualizes the result.<br />
[[File:mnist_figure.png|center|500px]]<br />
A publicly available pre-trained deep belief net resulted in 118 errors, and it was reduced to 92 errors when the model was fine-tuned with dropout. Another publicly available model was a deep Boltzmann machine, and it resulted in 103, 97, 94, 93 and 88 when the model was fine-tuned using standard backpropagation and was unrolled. They were reduced to 83, 79, 78, 78, and 77 when the model was fine-tuned with dropout – the mean of 79 errors was a record for models that do not use prior knowledge or enhanced training sets.<br />
<br />
= TIMIT = <br />
<br />
TIMIT dataset includes voice samples of 630 American English speakers varying across 8 different dialects. It is often used to evaluate the performance of automatic speech recognition systems. Using Kaldi, the dataset was pre-processed to extract input features in the form of log filter bank responses.<br />
<br />
=== Pre-training and Training ===<br />
<br />
For pretraining, they pretrained their neural network with a deep belief network and the first layer was built using Restricted Boltzmann Machine (RBM). Initializing visible biases with zero, weights were sampled from random numbers that followed normal distribution <math>N(0, 0.01)</math>. Each visible neuron’s variance was set to 1.0 and remained unchanged.<br />
<br />
Minimizing Contrastive Divergence (CD) was used to facilitate learning. Since momentum is used to speed up learning, it was initially set to 0.5 and increased linearly to 0.9 over 20 epochs. The average gradient had 0.001 of a learning rate which was then multiplied by <math>(1-momentum)</math> and L2 weight decay was set to 0.001. After setting up the hyperparameters, the model was done training after 100 epochs. Binary RBMs were used for training all subsequent layers with a learning rate of 0.01. Then, <math>p</math> was set as the mean activation of a neuron in the data set and the visible bias of each neuron was initialized to <math>log(p/(1 − p))</math>. Training each layer with 50 epochs, all remaining hyper-parameters were the same as those for the Gaussian RBM.<br />
<br />
=== Dropout tuning ===<br />
<br />
The initial weights were set in a neural network from the pretrained RBMs. To finetune the network with dropout-backpropagation, momentum was initially set to 0.5 and increased linearly up to 0.9 over 10 epochs. The model had a small constant learning rate of 1.0 and it was used to apply to the average gradient on a minibatch. The model also retained all other hyperparameters the same as the model from MNIST dropout finetuning. The model required approximately 200 epochs to converge. For comparison purpose, they also finetuned the same network with standard backpropagation with a learning rate of 0.1 with the same hyperparameters.<br />
<br />
=== Classification Test and Performance ===<br />
<br />
A Neural network was constructed to output the classification error rate on the test set of TIMIT dataset. They have built the neural network with four fully-connected hidden layers with 4000 neurons per layer. The output layer distinguishes distinct classes from 185 softmax output neurons that are merged into 39 classes. After constructing the neural network, 21 adjacent frames with an advance of 10ms per frame was given as an input.<br />
<br />
Comparing the performance of dropout with standard backpropagation on several network architectures and input representations, dropout consistently achieved lower error and cross-entropy. Results showed that it significantly controls overfitting, making the method robust to choices of network architecture. It also allowed much larger nets to be trained and removed the need for early stopping. Thus, neural network architectures with dropout are not very sensitive to the choice of learning rate and momentum.<br />
<br />
= Reuters Corpus Volume =<br />
Reuters Corpus Volume I archives 804,414 news documents that belong to 103 topics. Under four major themes - corporate/industrial, economics, government/social, and markets – they belonged to 63 classes. After removing 11 classes with no data and one class with insufficient data, they are left with 50 classes and 402,738 documents. The documents were divided into training and test sets equally and randomly, with each document representing the 2000 most frequent words in the dataset, excluding stopwords.<br />
<br />
They trained two neural networks, with size 2000-2000-1000-50, one using dropout and backpropagation, and the other using standard backpropagation. The training hyperparameters are the same as that in MNIST, but training was done for 500 epochs.<br />
<br />
In the following figure, we see the significant improvements by the model with dropout in the test set error. On the right side, we see that the learning with dropout also proceeds smoother. <br />
<br />
[[File:reuters_figure.png|700px|center]]<br />
<br />
= CNN =<br />
<br />
Feed-forward neural networks consist of several layers of neurons where each neuron in a layer applies a linear filter to the input image data and is passed on to the neurons in the next layer. When calculating the neuron’s output, scalar bias aka weights is applied to the filter with nonlinear activation function as parameters of the network that are learned by training data. [[File:cnnbigpicture.jpeg|thumb|upright=2|center|alt=text|Figure: Overview of Convolutional Neural Network]] There are several differences between Convolutional Neural networks and ordinary neural networks. The figure above gives a visual representation of a Convolutional Neural Network. First, CNN’s neurons are organized topographically into a bank and laid out on a 2D grid, so it reflects the organization of dimensions of the input data. Secondly, neurons in CNN apply filters which are local, and which are centered at the neuron’s location in the topographic organization. Meaning that useful metrics or clues to identify the object in an input image which can be found by examining local neighborhoods of the image. Next, all neurons in a bank apply the same filter at different locations in the input image. When looking at the image example, green is an input to one neuron bank, yellow is filter bank, and pink is the output of one neuron bank (convolved feature). A bank of neurons in a CNN applies a convolution operation, aka filters, to its input where a single layer in a CNN typically has multiple banks of neurons, each performing a convolution with a different filter. The resulting neuron banks become distinct input channels into the next layer. The whole process reduces the net’s representational capacity, but also reduces the capacity to overfit.<br />
[[File:bankofneurons.gif|thumb|upright=3|center|alt=text|Figure: Bank of neurons]]<br />
<br />
=== Pooling ===<br />
<br />
Pooling layer summarizes the activities of local patches of neurons in the convolutional layer by subsampling the output of a convolutional layer. Pooling is useful for extracting dominant features, to decrease the computational power required to process the data through dimensionality reduction. The procedure of pooling goes on like this; output from convolutional layers is divided into sections called pooling units and they are laid out topographically, connected to a local neighborhood of other pooling units from the same convolutional output. Then, each pooling unit is computed with some function which could be maximum and average. Maximum pooling returns the maximum value from the section of the image covered by the pooling unit while average pooling returns the average of all the values inside the pooling unit (see example). In result, there are fewer total pooling units than convolutional unit outputs from the previous layer, this is due to larger spacing between pixels on pooling layers. Using the max-pooling function reduces the effect of outliers and improves generalization.<br />
[[File:maxandavgpooling.jpeg|thumb|upright=2|center|alt=text|Figure: Max pooling and Average pooling]]<br />
<br />
=== Local Response Normalization === <br />
<br />
This network includes local response normalization layers which are implemented in lateral form and used on neurons with unbounded activations and permits the detection of high-frequency features with a big neuron response. This regularizer encourages competition among neurons belonging to different banks. Normalization is done by dividing the activity of a neuron in bank <math>i</math> at position <math>(x,y)</math> by the equation:<br />
[[File:local response norm.png|upright=2|center|]] where the sum runs over <math>N</math> ‘adjacent’ banks of neurons at the same position as in the topographic organization of neuron bank. The constants, <math>N</math>, <math>alpha</math> and <math>betas</math> are hyper-parameters whose values are determined using a validation set. This technique is replaced by better techniques such as the combination of dropout and regularization methods (<math>L1</math> and <math>L2</math>)<br />
<br />
=== Neuron nonlinearities ===<br />
<br />
All of the neurons for this model use the max-with-zero nonlinearity where output within a neuron is computed as <math> a^{i}_{x,y} = max(0, z^i_{x,y})</math> where <math> z^i_{x,y} </math> is the total input to the neuron. The reason they use nonlinearity is because it has several advantages over traditional saturating neuron models, such as significant reduction in training time required to reach a certain error rate. Another advantage is that nonlinearity reduces the need for contrast-normalization and data pre-processing since neurons do not saturate- meaning activities simply scale up little by little with usually large input values. For this model’s only pre-processing step, they subtract the mean activity from each pixel and the result is a centered data.<br />
<br />
=== Objective function ===<br />
<br />
The objective function of their network maximizes the multinomial logistic regression objective which is the same as minimizing the average cross-entropy across training cases between the true label and the model’s predicted label.<br />
<br />
=== Weight Initialization === <br />
<br />
It’s important to note that if a neuron always receives a negative value during training, it will not learn because its output is uniformly zero under the max-with-zero nonlinearity. Hence, the weights in their model were sampled from a zero-mean normal distribution with a high enough variance. High variance in weights will set a certain number of neurons with positive values for learning to happen, and in practice, it’s necessary to try out several candidates for variances until a working initialization is found. In their experiment, setting a positive constant, or 1, as biases of the neurons in the hidden layers was helpful in finding it.<br />
<br />
=== Training ===<br />
<br />
In this model, a batch size of 128 samples and momentum of 0.9, we train our model using stochastic gradient descent. The update rule for weight <math>w</math> is $$ v_{i+1} = 0.9v_i + \epsilon <\frac{dE}{dw_i}> i$$ $$w_{i+1} = w_i + v_{i+1} $$ where <math>i</math> is the iteration index, <math>v</math> is a momentum variable, <math>\epsilon</math> is the learning rate and <math>\frac{dE}{dw}</math> is the average over the <math>i</math>th batch of the derivative of the objective with respect to <math>w_i</math>. The whole training process on CIFAR-10 takes roughly 90 minutes and ImageNet takes 4 days with dropout and two days without.<br />
<br />
=== Learning ===<br />
To determine the learning rate for the network, it is a must to start with an equal learning rate for each layer which produces the largest reduction in the objective function with power of ten. Usually, it is in the order of <math>10^{-2}</math> or <math>10^{-3}</math>. In this case, they reduce the learning rate twice by a factor of ten before termination of training.<br />
<br />
= CIFAR-10 =<br />
<br />
=== CIFAR-10 Dataset ===<br />
<br />
Removing incorrect labels, The CIFAR-10 dataset is a subset of the Tiny Images dataset with 10 classes. It contains 5000 training images and 1000 testing images for each class. The dataset has 32 x 32 color images searched from the web and the images are labeled with the noun used to search the image.<br />
<br />
[[File:CIFAR-10.png|thumb|upright=2|center|alt=text|Figure 4: CIFAR-10 Sample Dataset]]<br />
<br />
=== Models for CIFAR-10 ===<br />
<br />
Two models, one with dropout and one without dropout, were built to test the performance of dropout on CIFAR-10. All models have CNN with three convolutional layers each with a pooling layer. The max-pooling method is performed by the pooling layer which follows the first convolutional layer, and the average-pooling method is performed by remaining 2 pooling layers. The first and second pooling layers with <math>N = 9, α = 0.001</math>, and <math>β = 0.75</math> are followed by response normalization layers. A ten-unit softmax layer, which is used to output a probability distribution over class labels, is connected with the upper-most pooling layer. Using filter size of 5×5, all convolutional layers have 64 filter banks.<br />
<br />
Additional changes were made with the model with dropout. The model with dropout enables us to use more parameters because dropout forces a strong regularization on the network. Thus, a fourth weight layer is added to take the input from the previous pooling layer. This fourth weight layer is locally connected, but not convolutional, and contains 16 banks of filters of size 3 × 3 with 50% dropout. Lastly, the softmax layer takes its input from this fourth weight layer.<br />
<br />
Thus, with a neural network with 3 convolutional hidden layers with 3 max-pooling layers, the classification error achieved 16.6% to beat 18.5% from the best published error rate without using transformed data. The model with one additional locally-connected layer and dropout at the last hidden layer produced the error rate of 15.6%.<br />
<br />
= ImageNet =<br />
<br />
===ImageNet Dataset===<br />
<br />
ImageNet is a dataset of millions of high-resolution labeled images in thousands of categories which were collected from the web and labelled by human labellers using MTerk tool (Amazon’s Mechanical Turk crowd-sourcing tool). Because this dataset has millions of labeled images in thousands of categories, it is very difficult to have perfect accuracy on this dataset even for humans because the ImageNet images may contain multiple objects and there are a large number of object classes. ImageNet and CIFAR-10 are very similar, but the scale of ImageNet is about 20 times bigger (1,300,000 vs 60,000). The size of ImageNet is about 1.3 million training images, 50,000 validation images, and 150,000 testing images. They used resized images of 256 x 256 pixels for their experiments.<br />
<br />
'''An ambiguous example to classify:'''<br />
<br />
[[File:imagenet1.png|200px|center]]<br />
<br />
When this paper was written, the best score on this dataset was the error rate of 45.7% by High-dimensional signature compression for large-scale image classification (J. Sanchez, F. Perronnin, CVPR11 (2011)). The authors of this paper could achieve a comparable performance of 48.6% error rate using a single neural network with five convolutional hidden layers with a max-pooling layer in between, followed by two globally connected layers and a final 1000-way softmax layer. When applying 50% dropout to the 6th layer, the error rate was brought down to 42.4%.<br />
<br />
'''ImageNet Dataset:'''<br />
<br />
[[File:imagenet2.png|400px|center]]<br />
<br />
===Models for ImageNet===<br />
<br />
The models for ImageNet with dropout (the model without dropout had a similar approach, but there was a serious issue with overfitting): <br />
<br />
They used a convolutional neural network trained by 224×224 patches randomly extracted from the 256 × 256 images. This could reduce the network’s capacity to overfit the training data and helped generalization as a form of data augmentation. The method of averaging the prediction of the net on ten 224 × 224 patches of the 256 × 256 input image was used for a testing (patched at the center, four corners, and their horizontal reflections).<br />
<br />
To maximize the performance on the validation set, this complicated network architecture was used and it was found that dropout was very effective. Also, it was demonstrated that using non-convolutional higher layers with the number of parameters worked well with dropout, but it had a negative impact to the performance without dropout.<br />
<br />
[[File:modelh2.png|700px|center]] <br />
<br />
[[File:layer2.png|600px|center]]<br />
<br />
It was demonstrated that making a large number of decisions was important for the architecture of the net design for the speech recognition (TIMIT) and object recognition datasets (CIFAR-10 and ImageNet). A separate validation set which evaluated the performance of a large number of different architectures was used to make those decisions, and then they chose the best performance architecture with dropout on the validation set so that they could apply it to the real test set.<br />
<br />
= Critiques =<br />
It is a very brilliant idea to dropout half of the neurons to reduce co-adaptations. It is mentioned that for fully connected layers, dropout in all hidden layers works better than dropout in only one hidden layer. There is another paper Dropout: A Simple Way to Prevent Neural Networks from<br />
Overfitting[https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf] gives a more detailed explanation.<br />
<br />
It will be interesting to see how this paper could be used to prevent overfitting of LSTMs.<br />
<br />
= Conclusion =<br />
<br />
The authors have shown a consistent improvement by the models trained with dropout in classifying objects in the following datasets: MNIST; TIMIT; Reuters Corpus Volume I; CIFAR-10; and ImageNet.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&diff=48499Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms2020-11-30T19:33:39Z<p>Jdhchow: </p>
<hr />
<div><br />
== Presented by ==<br />
<br />
Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee<br />
<br />
== Introduction ==<br />
<br />
This paper presents an approach to detecting heart disease from ECG signals by fine-tuning the deep learning neural network, ConvNetQuake. For context, ConvNetQuake is a convolutional neural network, used by Perol, Gharbi, and Denolle [4], for Earthquake detection and location from a single waveform. A deep learning approach was used due to the model's ability to be trained using multiple GPUs and terabyte-sized datasets. This, in turn, creates a model that is robust against noise. The purpose of this paper is to provide detailed analyses of the contributions of the ECG leads on identifying heart disease, to show the use of multiple channels in ConvNetQuake enhances prediction accuracy, and to show that feature engineering is not necessary for any of the training, validation, or testing processes. In this area, the combination of data fusion and machine learning techniques exhibits great promise to healthcare innovation, and the analyses in this paper help further this realization. The benefits of translating knowledge between deep learning and its real-world applications in health are also illustrated.<br />
<br />
== Previous Work and Motivation ==<br />
<br />
The database used in previous works is the Physikalisch-Technische Bundesanstalt (PTB) database, which consists of ECG records. Previous papers used techniques, such as CNN, SVM, K-nearest neighbors, naïve Bayes classification, and ANN. From these instances, the paper observes several faults in the previous papers. The first being the issue that most papers use feature selection on the raw ECG data before training the model. Dabanloo and Attarodi [2] used various techniques such as ANN, K-nearest neighbors, and Naïve Bayes. However, they extracted two features, the T-wave integral and the total integral, to aid in localizing and detecting heart disease. Sharma and Sunkaria [3] used SVM and K-nearest neighbors as their classifier, but extracted various features using stationary wavelet transforms to decompose the ECG signal into sub-bands. The second issue is that papers that do not use feature selection would arbitrarily pick ECG leads for classification without rationale. For example, Liu et al. [1] used a deep CNN that uses 3 seconds of ECG signal from lead II at a time as input. The decision for using lead II compared to the other leads was not explained. <br />
<br />
The issue with feature selection is that it can be time-consuming and impractical with large volumes of data. The second issue with the arbitrary selection of leads is that it does not offer insight into why the lead was chosen and the contributions of each lead in the identification of heart disease. Thus, this paper addresses these two issues through implementing a deep learning model that does not rely on feature selection of ECG data and to quantify the contributions of each ECG and Frank lead in identifying heart disease.<br />
<br />
== Model Architecture ==<br />
<br />
The dataset, which was used to train, validate, and test the neural network models, consists of 549 ECG records taken from 290 unique patients. Each ECG record has a mean length of over 100 seconds.<br />
<br />
This Deep Neural Network model was created by modifying the ConvNetQuake model by adding 1D batch normalization layers.<br />
<br />
During the training stage, a 10-second long two-channel input was fed into the neural network. In order to ensure that the two channels were weighted equally, both channels were normalized. Besides, time invariance was incorporated by selecting the 10-second long segment randomly from the entire signal. <br />
<br />
The input layer is a 10-second long ECG signal. There are 8 hidden layers in this model, each of which consists of a 1D convolution layer with the ReLu activation function followed by a batch normalization layer. The output layer is a one-dimensional layer that uses the Sigmoid activation function.<br />
<br />
This model is trained by using batches of size 10. The learning rate is 10^-4. The ADAM optimizer is used. In training the model, the dataset is split into a train set, validation set, and test set with ratios 80-10-10.<br />
<br />
During the training process, the model was trained from scratch numerous times to avoid inserting unintended variation into the model by randomly initializing weights.<br />
<br />
The following images gives a visual representation of the model.<br />
<br />
[[File:architecture.png | thumb | center | 1000px | Model Architecture (Gupta et al., 2019)]]<br />
<br />
==Result== <br />
<br />
The paper first uses quantification of accuracies for single channels with 20-fold cross-validation, resulting in the highest individual accuracies: v5, v6, vx, vz, and ii. The researcher further investigated the accuracies for pairs of the top 5 highest individual channels using 20-fold cross-validation. They arrived at the conclusion of highest pairs accuracies to fed into a neural network is lead v6 and lead vz. They then use 100-fold cross validation on v6 and vz pair of channels, then compare outliers based on top 20, top 50 and total 100 performing models, finding that standard deviation is non-trivial and there are few models performed very poorly. <br />
<br />
Next, they discussed 2 factors affecting model performance evaluation: 1） Random train-val-test split might have effects on the performance of the model, but it can be improved by access with a larger data set and further discussion; and 2） random initialization of the weights of the neural network shows little effects on the performance of the model performance evaluation, because of showing high average results with a fixed train-val-test split. <br />
<br />
Comparing with other models in the other 12 papers, the model in this article has the highest accuracy, specificity, and precision. With concerns of patients' records affecting the training accuracy, they used 290 fold patient-wise split, resulting in the same highest accuracy of the pair v6 and vz same as record-wise split. The second best pair was ii and vz, which also contains the vz channel. Combining the two best pair channels into v6, vz, vii ultimately gave the best results over 10 trials which has an average of 97.83% in patient-wise split. Even though the patient-wise split might result in lower accuracy evaluation, however, it still maintains a very high average.<br />
<br />
==Conclusion & Discussion== <br />
<br />
The paper introduced a new architecture for heart condition classification based on raw ECG signals using multiple leads. It outperformed the state-of-art model by a large margin of 1 percent. This study finds that out of the 15 ECG channels(12 conventional ECG leads and 3 Frank Leads), channel v6, vz, and ii contain the most meaningful information for detecting myocardial infraction. Also, recent advances in machine learning can be leveraged to produce a model capable of classifying myocardial infraction with a cardiologist-level success rate. To further improve the performance of the models, access to a larger labeled data set is needed. The PTB database is small. It is difficult to test the true robustness of the model with a relatively small test set. If a larger data set can be found to help correctly identify other heart conditions beyond myocardial infraction, the research group plans to share the deep learning models and develop an open-source, computationally efficient app that can be readily used by cardiologists.<br />
<br />
A detailed analysis of the relative importance of each of the 15 ECG channels indicates that deep learning can identify myocardial infraction by processing only ten seconds of raw ECG data from the v6, vz and ii leads and reaches a cardiologist-level success rate. Deep learning algorithms may be readily used as commodity software. The neural network model that was originally designed to identify earthquakes may be re-designed and tuned to identify myocardial infraction. Feature engineering of ECG data is not required to identify myocardial infraction in the PTB database. This model only required ten seconds of raw ECG data to identify this heart condition with cardiologist-level performance. Access to a larger database should be provided to deep learning researchers so they can work on detecting different types of heart conditions. Deep learning researchers and the cardiology community can work together to develop deep learning algorithms that provide trustworthy, real-time information regarding heart conditions with minimal computational resources.<br />
<br />
Fourier Transform(such as FFT) can be helpful when dealing with ECG signals. It transforms signals from time domain to frequency domain, which means some hidden features in frequency may be discovered.<br />
<br />
==Critiques==<br />
- The lack of large, labelled data sets is often a common problem in most applied deep learning studies. Since the PTB database is as small as you describe it to be, the robustness of the model which may be hard to gauge. There are very likely various other physical factors that may play a role in the study which the deep neural network may not be able to adjust for as well, since health data can be somewhat subjective at times and/or may be somewhat inaccurate, especially if machines are used to measurement. This might mean error was propagated forward in the study.<br />
<br />
- Additionally, there is a risk of confirmation bias, which may occur when a model is self-training, especially given the fact that the training set is small.<br />
<br />
- I feel that the results of deep learning models in medical settings where the consequences of misclassification can be severe should be evaluated by assigning weights to classification. In case if the misclassification can lead to severe consequences, then the network should be trained in such a way that it errs towards safety. For example, in case if heart disease, the consequences will be very high if the system says that there is no heart disease when in fact there is. So, the evaluation metric must be selected carefully.<br />
<br />
- This is a useful and meaningful application topic in machine learning. Using Deep Learning to detect heart disease can be very helpful if it is difficult to detect disease by looking at ECG by humans eys. This model also useful for doing statistics, such as calculating the percentage of people get heart disease. But I think the doctor should not 100% trust the result from the model, it is almost impossible to get 100% accuracy from a model. So, I think double-checking by human eyes is necessary if the result is weird. What is more, I think it will be interesting to discuss more applications in mediccal by using this method, such as detecting the Brainwave diagram to predict a person's mood and to diagnose mental diseases.<br />
<br />
- Compared to the dataset for other topics such as object recognition, the PTB database is pretty small with only 549 ECG records. And these are highly unbiased(Table 1) with 4 records for myocarditis and 148 for myocardial infarction. Medical datasets can only be labeled by specialists. This is why these datasets are related small. It would be great if there will be a larger, more comprehensive dataset.<br />
<br />
- Only results using 20-fold cross validation were presented. It should be shown that the results could be reproduced using a more common number of folds like 5 or 10<br />
<br />
- There are potential issues with the inclusion of Frank leads. From a practitioner standpoint, ECGs taken with Frank leads are less common. This could prevent the use of this technique. Additionally, Frank leads are expressible as a linear combinations of the 12 traditional leads. The authors are not adding any fundamentally new information by including them and their inclusion could be viewed as a form of feature selection (going against the authors' original intentions).<br />
<br />
== References ==<br />
<br />
[1] Na Liu et al. "A Simple and Effective Method for Detecting Myocardial Infarction Based on Deep Convolutional Neural Network". In: Journal of Medical Imaging and Health Informatics (Sept. 2018). doi: 10.1166/jmihi.2018.2463.<br />
<br />
[2] Naser Safdarian, N.J. Dabanloo, and Gholamreza Attarodi. "A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal". In: J. Biomedical Science and Engineering (Aug. 2014). doi: http://dx.doi.org/10.4236/jbise.2014.710081.<br />
<br />
[3] L.D. Sharma and R.K. Sunkaria. "Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach." In: Signal, Image and Video Processing (July 2017). doi: https://doi.org/10.1007/s11760-017-1146-z.<br />
<br />
[4] Perol Thibaut, Gharbi Michaël, and Denolle Marin. "Convolutional neural network for earthquake detection and location". In: Science Advances (Feb. 2018). doi: 10.1126/sciadv.1700578</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&diff=48497Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms2020-11-30T19:32:21Z<p>Jdhchow: </p>
<hr />
<div><br />
== Presented by ==<br />
<br />
Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee<br />
<br />
== Introduction ==<br />
<br />
This paper presents an approach to detecting heart disease from ECG signals by fine-tuning the deep learning neural network, ConvNetQuake. For context, ConvNetQuake is a convolutional neural network, used by Perol, Gharbi, and Denolle [4], for Earthquake detection and location from a single waveform. A deep learning approach was used due to the model's ability to be trained using multiple GPUs and terabyte-sized datasets. This, in turn, creates a model that is robust against noise. The purpose of this paper is to provide detailed analyses of the contributions of the ECG leads on identifying heart disease, to show the use of multiple channels in ConvNetQuake enhances prediction accuracy, and to show that feature engineering is not necessary for any of the training, validation, or testing processes. In this area, the combination of data fusion and machine learning techniques exhibits great promise to healthcare innovation, and the analyses in this paper help further this realization. The benefits of translating knowledge between deep learning and its real-world applications in health are also illustrated.<br />
<br />
== Previous Work and Motivation ==<br />
<br />
The database used in previous works is the Physikalisch-Technische Bundesanstalt (PTB) database, which consists of ECG records. Previous papers used techniques, such as CNN, SVM, K-nearest neighbors, naïve Bayes classification, and ANN. From these instances, the paper observes several faults in the previous papers. The first being the issue that most papers use feature selection on the raw ECG data before training the model. Dabanloo and Attarodi [2] used various techniques such as ANN, K-nearest neighbors, and Naïve Bayes. However, they extracted two features, the T-wave integral and the total integral, to aid in localizing and detecting heart disease. Sharma and Sunkaria [3] used SVM and K-nearest neighbors as their classifier, but extracted various features using stationary wavelet transforms to decompose the ECG signal into sub-bands. The second issue is that papers that do not use feature selection would arbitrarily pick ECG leads for classification without rationale. For example, Liu et al. [1] used a deep CNN that uses 3 seconds of ECG signal from lead II at a time as input. The decision for using lead II compared to the other leads was not explained. <br />
<br />
The issue with feature selection is that it can be time-consuming and impractical with large volumes of data. The second issue with the arbitrary selection of leads is that it does not offer insight into why the lead was chosen and the contributions of each lead in the identification of heart disease. Thus, this paper addresses these two issues through implementing a deep learning model that does not rely on feature selection of ECG data and to quantify the contributions of each ECG and Frank lead in identifying heart disease.<br />
<br />
== Model Architecture ==<br />
<br />
The dataset, which was used to train, validate, and test the neural network models, consists of 549 ECG records taken from 290 unique patients. Each ECG record has a mean length of over 100 seconds.<br />
<br />
This Deep Neural Network model was created by modifying the ConvNetQuake model by adding 1D batch normalization layers.<br />
<br />
During the training stage, a 10-second long two-channel input was fed into the neural network. In order to ensure that the two channels were weighted equally, both channels were normalized. Besides, time invariance was incorporated by selecting the 10-second long segment randomly from the entire signal. <br />
<br />
The input layer is a 10-second long ECG signal. There are 8 hidden layers in this model, each of which consists of a 1D convolution layer with the ReLu activation function followed by a batch normalization layer. The output layer is a one-dimensional layer that uses the Sigmoid activation function.<br />
<br />
This model is trained by using batches of size 10. The learning rate is 10^-4. The ADAM optimizer is used. In training the model, the dataset is split into a train set, validation set, and test set with ratios 80-10-10.<br />
<br />
During the training process, the model was trained from scratch numerous times to avoid inserting unintended variation into the model by randomly initializing weights.<br />
<br />
The following images gives a visual representation of the model.<br />
<br />
[[File:architecture.png | thumb | center | 1000px | Model Architecture (Gupta et al., 2019)]]<br />
<br />
==Result== <br />
<br />
The paper first uses quantification of accuracies for single channels with 20-fold cross-validation, resulting in the highest individual accuracies: v5, v6, vx, vz, and ii. The researcher further investigated the accuracies for pairs of the top 5 highest individual channels using 20-fold cross-validation. They arrived at the conclusion of highest pairs accuracies to fed into a neural network is lead v6 and lead vz. They then use 100-fold cross validation on v6 and vz pair of channels, then compare outliers based on top 20, top 50 and total 100 performing models, finding that standard deviation is non-trivial and there are few models performed very poorly. <br />
<br />
Next, they discussed 2 factors affecting model performance evaluation: 1） Random train-val-test split might have effects on the performance of the model, but it can be improved by access with a larger data set and further discussion; and 2） random initialization of the weights of the neural network shows little effects on the performance of the model performance evaluation, because of showing high average results with a fixed train-val-test split. <br />
<br />
Comparing with other models in the other 12 papers, the model in this article has the highest accuracy, specificity, and precision. With concerns of patients' records affecting the training accuracy, they used 290 fold patient-wise split, resulting in the same highest accuracy of the pair v6 and vz same as record-wise split. The second best pair was ii and vz, which also contains the vz channel. Combining the two best pair channels into v6, vz, vii ultimately gave the best results over 10 trials which has an average of 97.83% in patient-wise split. Even though the patient-wise split might result in lower accuracy evaluation, however, it still maintains a very high average.<br />
<br />
==Conclusion & Discussion== <br />
<br />
The paper introduced a new architecture for heart condition classification based on raw ECG signals using multiple leads. It outperformed the state-of-art model by a large margin of 1 percent. This study finds that out of the 15 ECG channels(12 conventional ECG leads and 3 Frank Leads), channel v6, vz, and ii contain the most meaningful information for detecting myocardial infraction. Also, recent advances in machine learning can be leveraged to produce a model capable of classifying myocardial infraction with a cardiologist-level success rate. To further improve the performance of the models, access to a larger labeled data set is needed. The PTB database is small. It is difficult to test the true robustness of the model with a relatively small test set. If a larger data set can be found to help correctly identify other heart conditions beyond myocardial infraction, the research group plans to share the deep learning models and develop an open-source, computationally efficient app that can be readily used by cardiologists.<br />
<br />
A detailed analysis of the relative importance of each of the standard 15 ECG channels indicates that deep learning can identify myocardial infraction by processing only ten seconds of raw ECG data from the v6, vz and ii leads and reaches a cardiologist-level success rate. Deep learning algorithms may be readily used as commodity software. The neural network model that was originally designed to identify earthquakes may be re-designed and tuned to identify myocardial infraction. Feature engineering of ECG data is not required to identify myocardial infraction in the PTB database. This model only required ten seconds of raw ECG data to identify this heart condition with cardiologist-level performance. Access to a larger database should be provided to deep learning researchers so they can work on detecting different types of heart conditions. Deep learning researchers and the cardiology community can work together to develop deep learning algorithms that provide trustworthy, real-time information regarding heart conditions with minimal computational resources.<br />
<br />
Fourier Transform(such as FFT) can be helpful when dealing with ECG signals. It transforms signals from time domain to frequency domain, which means some hidden features in frequency may be discovered.<br />
<br />
==Critiques==<br />
- The lack of large, labelled data sets is often a common problem in most applied deep learning studies. Since the PTB database is as small as you describe it to be, the robustness of the model which may be hard to gauge. There are very likely various other physical factors that may play a role in the study which the deep neural network may not be able to adjust for as well, since health data can be somewhat subjective at times and/or may be somewhat inaccurate, especially if machines are used to measurement. This might mean error was propagated forward in the study.<br />
<br />
- Additionally, there is a risk of confirmation bias, which may occur when a model is self-training, especially given the fact that the training set is small.<br />
<br />
- I feel that the results of deep learning models in medical settings where the consequences of misclassification can be severe should be evaluated by assigning weights to classification. In case if the misclassification can lead to severe consequences, then the network should be trained in such a way that it errs towards safety. For example, in case if heart disease, the consequences will be very high if the system says that there is no heart disease when in fact there is. So, the evaluation metric must be selected carefully.<br />
<br />
- This is a useful and meaningful application topic in machine learning. Using Deep Learning to detect heart disease can be very helpful if it is difficult to detect disease by looking at ECG by humans eys. This model also useful for doing statistics, such as calculating the percentage of people get heart disease. But I think the doctor should not 100% trust the result from the model, it is almost impossible to get 100% accuracy from a model. So, I think double-checking by human eyes is necessary if the result is weird. What is more, I think it will be interesting to discuss more applications in mediccal by using this method, such as detecting the Brainwave diagram to predict a person's mood and to diagnose mental diseases.<br />
<br />
- Compared to the dataset for other topics such as object recognition, the PTB database is pretty small with only 549 ECG records. And these are highly unbiased(Table 1) with 4 records for myocarditis and 148 for myocardial infarction. Medical datasets can only be labeled by specialists. This is why these datasets are related small. It would be great if there will be a larger, more comprehensive dataset.<br />
<br />
- Only results using 20-fold cross validation were presented. It should be shown that the results could be reproduced using a more common number of folds like 5 or 10<br />
<br />
- There are potential issues with the inclusion of Frank leads. From a practitioner standpoint, ECGs taken with Frank leads are less common. This could prevent the use of this technique. Additionally, Frank leads are expressible as a linear combinations of the 12 traditional leads. The authors are not adding any any fundamentally new information by including them. Their inclusion could be viewed as a form of feature selection (going against the authors' original intentions).<br />
<br />
== References ==<br />
<br />
[1] Na Liu et al. "A Simple and Effective Method for Detecting Myocardial Infarction Based on Deep Convolutional Neural Network". In: Journal of Medical Imaging and Health Informatics (Sept. 2018). doi: 10.1166/jmihi.2018.2463.<br />
<br />
[2] Naser Safdarian, N.J. Dabanloo, and Gholamreza Attarodi. "A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal". In: J. Biomedical Science and Engineering (Aug. 2014). doi: http://dx.doi.org/10.4236/jbise.2014.710081.<br />
<br />
[3] L.D. Sharma and R.K. Sunkaria. "Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach." In: Signal, Image and Video Processing (July 2017). doi: https://doi.org/10.1007/s11760-017-1146-z.<br />
<br />
[4] Perol Thibaut, Gharbi Michaël, and Denolle Marin. "Convolutional neural network for earthquake detection and location". In: Science Advances (Feb. 2018). doi: 10.1126/sciadv.1700578</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Loss_Function_Search_for_Face_Recognition&diff=48480Loss Function Search for Face Recognition2020-11-30T19:06:02Z<p>Jdhchow: </p>
<hr />
<div>== Presented by ==<br />
Jan Lau, Anas Mahdi, Will Thibault, Jiwon Yang<br />
<br />
== Introduction ==<br />
Face recognition is a technology that can label a face to a specific identity. The process involves two tasks: 1. Identifying and classifying a face to a certain identity and 2. Verifying if this face and another face map to the same identity. Loss functions play an important role in evaluating how well the prediction models the given data. In the application of face recognition, they are used for training convolutional neural networks (CNNs) with discriminative features. However, traditional softmax loss lacks the power of feature discrimination. To solve this problem, a center loss was developed to learn centers for each identity to enhance the intra-class compactness.<br />
<br />
Hence, the paper introduced a new loss function which can reduce the softmax probability. Softmax probability is the probability for each class. It contains a vector of values that add up to 1 while ranging between 0 and 1. Cross-entropy loss is the negative log of the probabilities. When softmax probability is combined with cross-entropy loss in the last fully connected layer of the CNN, it yields the softmax loss function:<br />
<br />
<center><math>L_1=-log\frac{e^{w^T_yx}}{e^{w^T_yx} + \sum_{k≠y}^K{e^{w^T_yx}}}</math> [1] </center><br />
<br />
Specifically for face recognition, <math>L_1</math> is modified such that <math>w^T_yx</math> is normalized and <math>s</math> represents the magnitude of <math>w^T_yx</math>:<br />
<br />
<center><math>L_2=-log\frac{e^{s cos{(\theta_{{w_y},x})}}}{e^{s cos{(\theta_{{w_y},x})}} + \sum_{k≠y}^K{e^{s cos{(\theta_{{w_y},x})}}}}</math> [1] </center><br />
<br />
This function is crucial in face recognition because it is used for enhancing feature discrimination. While there are different variations of the softmax loss function, they build upon the same structure as the equation above. Some of these variations will be discussed in detail in the later sections. <br />
<br />
In this paper, the authors first identified that reducing the softmax probability is a key contribution to feature discrimination and designed two design search spaces (random and reward-guided method). They then evaluated their Random-Softmax and Search-Softmax approaches by comparing the results against other face recognition algorithms using nine popular face recognition benchmarks.<br />
<br />
== Previous Work ==<br />
Margin-based (angular, additive, additive angular margins) soft-max loss functions are important in learning discriminative features in face recognition. There have been hand-crafted methods previously developed that require much efforts such as A-softmax, V-softmax, AM-Softmax, and Arc-softmax. Li et al. proposed an AutoML for loss function search method also known as AM-LFS from a hyper-parameter optimization perspective [2]. It automatically determines the search space by leveraging reinforcement learning to the search loss functions during the training process, though the drawback is the complex and unstable search space.<br />
<br />
== Motivation ==<br />
Previous algorithms for facial recognition frequently rely on CNNs that may include metric learning loss functions such as contrastive loss or triplet loss. Without sensitive sample mining strategies, the computational cost for these functions was high. This drawback prompts the redesign of classical softmax loss that cannot discriminate features. Multiple softmax loss functions have since been developed, and including margin-based formulations, they often require fine-tuning of parameters and are susceptible to instability. Therefore, researchers need to put in a lot of effort in creating their method in the large design space. AM-LFS takes an optimization approach for selecting hyperparameters for the margin-based softmax functions, but its aforementioned drawbacks are caused by the lack of direction in designing the search space.<br />
<br />
To solve the issues associated with hand-tuned softmax loss functions and AM-LFS, the authors attempt to reduce the softmax probability to improve feature discrimination when using margin-based softmax loss functions. The development of margin-based softmax loss with only one parameter required and an improved search space using a reward-based method which allows the authors to determine the best option for their loss function.<br />
<br />
== Problem Formulation ==<br />
=== Analysis of Margin-based Softmax Loss ===<br />
Based on the softmax probability and the margin-based softmax probability, the following function can be developed [1]:<br />
<br />
<center><math>p_m=\frac{1}{ap+(1-a)}*p</math></center><br />
<center> where <math>a=1-e^{s{cos{(\theta_{w_y},x)}-f{(m,\theta_{w_y},x)}}}</math> and <math>a≤0</math></center><br />
<br />
<math>a</math> is considered as a modulating factor and <math>h{(a,p)}=\frac{1}{ap+(1-a)} \in (0,1]</math> is a modulating function [1]. Therefore, regardless of the margin function (<math>f</math>), the minimization of the softmax probability will ensure success.<br />
<br />
Compared to AM-LFS, this method involves only one parameter (<math>a</math>) that is also constrained, versus AM-LFS which has 2M parameters without constraints that specify the piecewise linear functions the method requires. Also, the piecewise linear functions of AM-LFS (<math>p_m={a_i}p+b_i</math>) may not be discriminative because it could be larger than the softmax probability.<br />
<br />
=== Random Search ===<br />
Unified formulation <math>L_5</math> is generated by inserting a simple modulating function <math>h{(a,p)}=\frac{1}{ap+(1-a)}</math> into the original softmax loss. It can be written as below [1]:<br />
<br />
<center><math>L_5=-log{(h{(a,p)}*p)}</math> where <math>h \in (0,1]</math> and <math>a≤0</math></center><br />
<br />
This encourages the feature margin between different classes and has the capability of feature discrimination. This leads to defining the search space as the choice of <math>h{(a,p)}</math> whose impacts on the training procedure are decided by the modulating factor <math>a</math>. In order to validate the unified formulation, a modulating factor is randomly set at each training epoch. This is noted as Random-Softmax in this paper.<br />
<br />
=== Reward-Guided Search ===<br />
Unlike supervised learning, reinforcement learning (RL) is a behavioral learning model. It does not need to have input/output labelled and it does not need a sub-optimal action to be explicitly corrected. The algorithm receives feedback from the data to achieve the best outcome. The system has an agent that guides the process by taking an action that maximizes the notion of cumulative reward [3]. The process of RL is shown in figure 1. The equation of the cumulative reward function is: <br />
<br />
<center><math>G_t \overset{\Delta}{=} R_t+R_{t+1}+R_{t+2}+⋯+R_T</math></center><br />
<br />
where <math>G_t</math> = cumulative reward, <math>R_t</math> = immediate reward, and <math>R_T</math> = end of episode.<br />
<br />
<math>G_t</math> is the sum of immediate rewards from arbitrary time <math>t</math>. It is a random variable because it depends on the immediate reward which depends on the agent action and the environment's reaction to this action.<br />
<br />
<center>[[Image:G25_Figure1.png|300px |link=https://en.wikipedia.org/wiki/Reinforcement_learning#/media/File:Reinforcement_learning_diagram.svg |alt=Alt text|Title text]]</center><br />
<center>Figure 1: Reinforcement Learning scenario [4]</center><br />
<br />
The reward function is what guides the agent to move in a certain direction. As mentioned above, the system receives feedback from the data to achieve the best outcome. This is caused by the reward being edited based on the feedback it receives when a task is completed [5]. <br />
<br />
In this paper, RL is being used to generate a distribution of the hyperparameter <math>\mu</math> for the SoftMax equation using the reward function. <math>\mu</math> updates after each epoch from the reward function. <br />
<br />
<center><math>\mu_{e+1}=\mu_e + \eta \frac{1}{B} \sum_{i=1}^B R{(a_i)}{\nabla_a}log{(g(a_i;\mu,\sigma))}</math></center><br />
<br />
=== Optimization ===<br />
Calculating the reward involves a standard bi-level optimization problem, which involves a hyperparameter ({<math>a_1,a_2,…,a_B</math>}) that can be used for minimizing one objective function while maximizing another objective function simultaneously:<br />
<br />
<center><math>max_a R(a)=r(M_{w^*(a)},S_v)</math></center><br />
<center><math>w^*(a)=_w \sum_{(x,y) \in S_t} L^a (M_w(x),y)</math></center><br />
<br />
In this case, the loss function takes the training set <math>S_t</math> and the reward function takes the validation set <math>S_v</math>. The weights <math>w</math> are trained such that the loss function is minimized while the reward function is maximized. The calculated reward for each model ({<math>M_{we1},M_{we2},…,M_{weB}</math>}) yields the corresponding score, then the algorithm chooses the one with the highest score for model index selection. With the model containing the highest score being used in the next epoch, this process is repeated until the training reaches convergence. In the end, the algorithm takes the model with the highest score without retraining.<br />
<br />
== Results and Discussion ==<br />
=== Data Preprocessing ===<br />
The training datasets consisted of cleaned versions of CASIA-WebFace and MS-Celeb-1M-v1c to remove the impact of noisy labels in the original sets. Furthermore, there were a total of 15,414 identities that overlapped between the testing and training datasets. These were removed from the training sets.<br />
<br />
=== Results on LFW, SLLFW, CALFW, CPLFW, AgeDB, DFP ===<br />
For LFW, there is not a noticeable difference between the algorithms proposed in this paper and the other algorithms, however, AM-Softmax achieved higher results than Search-Softmax. Random-Softmax achieved the highest results by 0.03%.<br />
<br />
Random-Softmax outperforms baseline Soft-max and is comparable to most of the margin-based softmax. Search-Softmax boost the performance and better most methods specifically when training CASIA-WebFace-R data set, it achieves 0.72% average improvement over AM-Softmax. The reason the model proposed by the paper gives better results is because of their optimization strategy which helps boost the discimination power. Also the sampled candidate from the paper’s proposed search space can well approximate the margin-based loss functions. More tests need to happen to more complicated protocols to test the performance further. Not a lot of improvement has been shown on those test sets, since they are relatively simple and the performance of all the methods on these test sets are near saturation. The following table gives a summary of the performance of each model.<br />
<br />
<center>Table 1.Verification performance (%) of different methods on the test sets LFW, SLLFW, CALFW, CPLFW, AgeDB and CFP. The training set is '''CASIA-WebFace-R''' [1].</center><br />
<br />
<center>[[Image:G25_Table1.png|900px |alt=Alt text|Title text]]</center><br />
<br />
=== Results on RFW ===<br />
The RFW dataset measures racial bias which consists of Caucasian, Indian, Asian, and African. Using this as the test set, Random-softmax and Search-softmax performed better than the other methods. Random-softmax outperforms the baseline softmax by a large margin which means reducing the softmax probability will enhance the feature discrimination for face recognition. It is also observed that the reward guided search-softmax method is more likely to enhance the discriminative feature learning resulting in higher performance as shown in Table 2 and Table 3. <br />
<br />
<center>Table 2. Verification performance (%) of different methods on the test set RFW. The training set is '''CASIA-WebFace-R''' [1].</center><br />
<center>[[Image:G25_Table2.png|500px |alt=Alt text|Title text]]</center><br />
<br />
<br />
<center>Table 3. Verification performance (%) of different methods on the test set RFW. The training set is '''MS-Celeb-1M-v1c-R''' [1].</center><br />
<center>[[Image:G25_Table3.png|500px |alt=Alt text|Title text]]</center><br />
<br />
=== Results on MegaFace and Trillion-Pairs ===<br />
The different loss functions are tested again with more complicated protocols. The identification (Id.) Rank-1 and the verification (Veri.) with the true positive rate (TPR) at low false acceptance rate (FAR) at <math>1e-3</math> on MegaFace, the identification TPR@FAR = <math>1e-6</math> and the verification TPR@FAR = <math>1e-9</math> on Trillion-Pairs are reported on Table 4 and 5.<br />
<br />
On the test sets MegaFace and Trillion-Pairs, Search-softmax achieves the best performance over all other alternative methods. On MegaFace, Search-softmax beat the best competitor AM-softmax by a large margin. It also outperformed AM-LFS due to new designed search space. <br />
<br />
<center>Table 4. Performance (%) of different loss functions on the test sets MegaFace and Trillion-Pairs. The training set is '''CASIA-WebFace-R''' [1].</center><br />
<center>[[Image:G25_Table4.png|450px |alt=Alt text|Title text]]</center><br />
<br />
<br />
<center>Table 5. Performance (%) of different loss functions on the test sets MegaFace and Trillion-Pairs. The training set is '''MS-Celeb-1M-v1c-R''' [1].</center><br />
<center>[[Image:G25_Table5.png|450px |alt=Alt text|Title text]]</center><br />
<br />
From the CMC curves and ROC curves in Figure 2, similar trends are observed at other measures. There is a same trend on Trillion-Pairs where Search-softmax loss is found to be superior with 4% improvements with CASIA-WebFace-R and 1% improvements with MS-Celeb-1M-v1c-R at both the identification and verification. Based on these experiments, Search-Softmax loss can perform well, especially with a low false positive rate and it shows a strong generalization ability for face recognition.<br />
<br />
<center>[[Image:G25_Figure2_left.png|450px |alt=Alt text|Title text]] [[Image:G25_Figure2_right.png|450px |alt=Alt text|Title text]]</center><br />
<center>Figure 2. From Left to Right: CMC curves and ROC curves on MegaFace Set with training set CASIA-WebFace-R, CMC curves and ROC curves on MegaFace Set with training set MS-Celeb-1M-v1c-R [1].</center><br />
<br />
== Conclusion ==<br />
In this paper, it is discussed that in order to enhance feature discrimination for face recognition, it is key to know how to reduce the softmax probability. To achieve this goal, unified formulation for the margin-based softmax losses is designed. Two search methods have been developed using a random and a reward-guided loss function and they were validated to be effective over six other methods using nine different test data sets. While these developed methods were generally more effective in increasing accuracy versus previous methods, there is very little difference between the two. It can be seen that Search-Softmax performs slightly better than Random-Softmax most of the time.<br />
<br />
== Critiques ==<br />
* Thorough experimentation and comparison of results to state-of-the-art provided a convincing argument.<br />
* Datasets used did require some preprocessing, which may have improved the results beyond what the method otherwise would.<br />
* AM-LFS was created by the authors for experimentation (the code was not made public) so the comparison may not be accurate.<br />
* The test data set they used to test Search-Softmax and Random-Softmax are simple and they saturate in other methods. So the results of their methods didn’t show many advantages since they produce very similar results. A more complicated data set needs to be tested to prove the method's reliability.<br />
* There is another paper Large-Margin Softmax Loss for Convolutional Neural Networks[https://arxiv.org/pdf/1612.02295.pdf] that provides a more detailed explanation about how to reduce margin-based softmax loss.<br />
* It is questionable when it comes to the accuracy of testing sets, as they only used the clean version of CASIA-WebFace and MS-Celeb-1M-vlc for training instead of these two training sets with noisy labels.<br />
* In a similar [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform paper], written by Tae-Hyun Oh et al., they also discuss an optimal loss function for face recognition. However, since in the other paper, they were doing face recognition from voice audio, the loss function used was slightly different than the ones discussed in this paper.<br />
* This model has many applications such as identifying disguised prisoners for police. But we need to do a good data preprocessing otherwise we might not get a good predicted result. But authors did not mention about the data preprocessing which is a key part of this model.<br />
<br />
== References ==<br />
[1] X. Wang, S. Wang, C. Chi, S. Zhang and T. Mei, "Loss Function Search for Face Recognition", in International Conference on Machine Learning, 2020, pp. 1-10.<br />
<br />
[2] Li, C., Yuan, X., Lin, C., Guo, M., Wu, W., Yan, J., and Ouyang, W. Am-lfs: Automl for loss function search. In Proceedings of the IEEE International Conference on Computer Vision, pp. 8410–8419, 2019.<br />
2020].<br />
<br />
[3] S. L. AI, “Reinforcement Learning algorithms - an intuitive overview,” Medium, 18-Feb-2019. [Online]. Available: https://medium.com/@SmartLabAI/reinforcement-learning-algorithms-an-intuitive-overview-904e2dff5bbc. [Accessed: 25-Nov-2020]. <br />
<br />
[4] “Reinforcement learning,” Wikipedia, 17-Nov-2020. [Online]. Available: https://en.wikipedia.org/wiki/Reinforcement_learning. [Accessed: 24-Nov-2020].<br />
<br />
[5] B. Osiński, “What is reinforcement learning? The complete guide,” deepsense.ai, 23-Jul-2020. [Online]. Available: https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/. [Accessed: 25-Nov-2020].</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Efficient_kNN_Classification_with_Different_Numbers_of_Nearest_Neighbors&diff=48478Efficient kNN Classification with Different Numbers of Nearest Neighbors2020-11-30T18:43:21Z<p>Jdhchow: </p>
<hr />
<div>== Presented by == <br />
Cooper Brooke, Daniel Fagan, Maya Perelman<br />
<br />
== Introduction == <br />
Traditional model-based classification approaches first use training observations to fit a model before predicting test samples. In contrast, the model-free k-nearest neighbors (kNNs) method classifies observations with a majority rules approach. The kNN assigns test data to their class containing their k closest training observations (neighbours). This method has become very popular due to its strong performance and simple implementation. <br />
<br />
There are two main approaches to conduct kNN classification. The first is to use a fixed k value to classify all test samples. The second is to use a different k value for each test sample. The former, while easy to implement, has proven to be impractical in machine learning applications. Therefore, interest lies in developing an efficient way to apply a different optimal k value for each test sample. The authors of this paper presented the kTree and k*Tree methods to solve this research question.<br />
<br />
== Previous Work == <br />
<br />
Previous work on finding an optimal fixed k value for all test samples is well-studied. Zhang et al. [1] incorporated a certainty factor measure to solve for an optimal fixed k. This resulted in the conclusion that k should be <math>\sqrt{n}</math> (where n is the number of training samples) when n > 100. The method Song et al.[2] explored involved selecting a subset of the most informative samples from neighbourhoods. Vincent and Bengio [3] took the unique approach of designing a k-local hyperplane distance to solve for k. Premachandran and Kakarala [4] had the solution of selecting a robust k using the consensus of multiple rounds of kNNs. These fixed k methods are valuable however are impractical for data mining and machine learning applications. <br />
<br />
Finding an efficient approach to assigning varied k values has also been previously studied. Tuning approaches such as the ones taken by Zhu et al. as well as Sahugara et al. have been popular. Zhu et al. [5] determined that optimal k values should be chosen using cross validation while Sahugara et al. [6] proposed using Monte Carlo validation to select varied k parameters. Other learning approaches such as those taken by Zheng et al. and Góra and Wojna also show promise. Zheng et al. [7] applied a reconstruction framework to learn suitable k values. Góra and Wojna [8] proposed using rule induction and instance-based learning to learn optimal k-values for each test sample. While all these methods are valid, their processes of either learning varied k values or scanning all training samples are time-consuming.<br />
<br />
== Motivation == <br />
<br />
Due to the previously mentioned drawbacks of fixed-k and current varied-k kNN classification, the paper’s authors sought to design a new approach to solve for different k values. The kTree and k*Tree approach seek to calculate optimal values of k while avoiding computationally costly steps such as cross-validation.<br />
<br />
A secondary motivation of this research was to ensure that the kTree method would perform better than kNN using fixed values of k given that running costs would be similar in this instance.<br />
<br />
== Approach == <br />
<br />
<br />
=== kTree Classification ===<br />
<br />
The proposed kTree method is illustrated by the following flow chart:<br />
<br />
[[File:Approach_Figure_1.png | center | 800x800px]]<br />
<br />
==== Reconstruction ====<br />
<br />
The first step is to use the training samples to reconstruct themselves. The goal of this is to find the matrix of correlations between the training samples themselves, <math>\textbf{W}</math>, such that the distance between an individual training sample and the corresponding correlation vector multiplied by the entire training set is minimized. This least square loss function where <math>\mathbf{X}</math> represents the training set can be written as:<br />
<br />
$$\begin{aligned}<br />
\mathop{min}_{\textbf{W}} \sum_{i=1}^n ||Xw_i - x_i||^2<br />
\end{aligned}$$<br />
<br />
In addition, an <math>l_1</math> regularization term multiplied by a tuning parameter, <math>\rho_1</math>, is added to ensure that sparse results are generated as the objective is to minimize the number of training samples that will eventually be depended on by the test samples. <br />
<br />
The least square loss function is then further modified to account for samples that have similar values for certain features yielding similar results. After some transformations, this second regularization term that has tuning parameter <math>\rho_2</math> is:<br />
<br />
$$\begin{aligned}<br />
R(W) = Tr(\textbf{W}^T \textbf{X}^T \textbf{LXW})<br />
\end{aligned}$$<br />
<br />
where <math>\mathbf{L}</math> is a Laplacian matrix that indicates the relationship between features.<br />
<br />
This gives a final objective function of:<br />
<br />
$$\begin{aligned}<br />
\mathop{min}_{\textbf{W}} \sum_{i=1}^n ||Xw_i - x_i||^2 + \rho_1||\textbf{W}|| + \rho_2R(\textbf{W})<br />
\end{aligned}$$<br />
<br />
Since this is a convex function, an iterative method can be used to optimize it to find the optimal solution <math>\mathbf{W^*}</math>.<br />
<br />
==== Calculate ''k'' for training set ====<br />
<br />
Each element <math>w_{ij}</math> in <math>\textbf{W*}</math> represents the correlation between the ith and jth training sample so if a value is 0, it can be concluded that the jth training sample has no effect on the ith training sample which means that it should not be used in the prediction of the ith training sample. Consequently, all non-zero values in the <math>w_{.j}</math> vector would be useful in predicting the ith training sample which gives the result that the number of these non-zero elements for each sample is equal to the optimal ''k'' value for each sample.<br />
<br />
For example, if there was a 4x4 training set where <math>\textbf{W*}</math> had the form:<br />
<br />
[[File:Approach_Figure_2.png | center | 300x300px]]<br />
<br />
The optimal ''k'' value for training sample 1 would be 2 since the correlation between training sample 1 and both training samples 2 and 4 is non-zero.<br />
<br />
==== Train a Decision Tree using ''k'' as the label ====<br />
<br />
In a normal decision tree, the target data is the labels themselves. In contrast, in the kTree method, the target data is the optimal ''k'' values for each sample that were solved for in the previous step. So this decision tree has the following form:<br />
<br />
[[File:Approach_Figure_3.png | center | 300x300px]]<br />
<br />
==== Making Predictions for Test Data ====<br />
<br />
The optimal ''k'' values for each testing sample are easily obtainable using the kTree solved for in the previous step. The only remaining step is to predict the labels of the testing samples by finding the majority class of the optimal ''k'' nearest neighbours across '''all''' of the training data.<br />
<br />
=== k*Tree Classification ===<br />
<br />
The proposed k*Tree method is illustrated by the following flow chart:<br />
<br />
[[File:Approach_Figure_4.png | center | 800x800px]]<br />
<br />
Clearly, this is a very similar approach to the kTree as the k*Tree method attempts to sacrifice very little in predictive power in return for a substantial decrease in complexity when actually implementing the traditional kNN on the testing data once the optimal ''k'' values have been found.<br />
<br />
While all steps previous are the exact same, the k*Tree method not only stores the optimal ''k'' value but also the following information:<br />
<br />
* The training samples that have the same optimal ''k''<br />
* The ''k'' nearest neighbours of the previously identified training samples<br />
* The nearest neighbor of each of the previously identified ''k'' nearest neighbours<br />
<br />
The data stored in each node is summarized in the following figure:<br />
<br />
[[File:Approach_Figure_5.png | center | 800x800px]]<br />
<br />
In the kTree method, predictions were made based on all of the training data, whereas in the k*Tree method, predicting the test labels will only be done using the samples stored in the applicable node of the tree.<br />
<br />
== Experiments == <br />
<br />
In order to assess the performance of the proposed method against existing methods, a number of experiments were performed to measure classification accuracy and run time. The experiments were run on twenty public datasets provided by the UCI Repository of Machine Learning Data, and contained a mix of data types varying in size, in dimensionality, in the number of classes, and in imbalanced nature of the data. Ten-fold cross-validation was used to measure classification accuracy, and the following methods were compared against:<br />
<br />
# k-Nearest Neighbor: The classical kNN approach with k set to k=1,5,10,20 and square root of the sample size [9]; the best result was reported.<br />
# kNN-Based Applicability Domain Approach (AD-kNN) [11]<br />
# kNN Method Based on Sparse Learning (S-kNN) [10]<br />
# kNN Based on Graph Sparse Reconstruction (GS-kNN) [7]<br />
# Filtered Attribute Subspace-based Bagging with Injected Randomness (FASBIR) [12], [13]<br />
# Landmark-based Spectral Clustering kNN (LC-kNN) [14]<br />
<br />
The experimental results were then assessed based on classification tasks that focused on different sample sizes, and tasks that focused on different numbers of features. <br />
<br />
<br />
'''A. Experimental Results on Different Sample Sizes'''<br />
<br />
The running cost and (cross-validation) classification accuracy based on experiments on ten UCI datasets can be seen in Table I below.<br />
<br />
[[File:Table_I_kNN.png | center | 800x800px]]<br />
<br />
The following key results are noted:<br />
* Regarding classification accuracy, the proposed methods (kTree and k*Tree) outperformed kNN, AD-KNN, FASBIR, and LC-kNN on all datasets by 1.5%-4.5%, but had no notable improvements compared to GS-kNN and S-kNN.<br />
* Classification methods which involved learning optimal k-values (for example the proposed kTree and k*Tree methods, or S-kNN, GS-kNN, AD-kNN) outperformed the methods with predefined k-values, such as traditional kNN.<br />
* The proposed k*Tree method had the lowest running cost of all methods. However, the k*Tree method was still outperformed in terms of classification accuracy by GS-kNN and S-kNN, but ran on average 15 000 times faster than either method. In addition, the kTree had the highest accuracy and it's running cost was lower than any other methods except the k*Tree method.<br />
<br />
<br />
'''B. Experimental Results on Different Feature Numbers'''<br />
<br />
The goal of this section was to evaluate the robustness of all methods under differing numbers of features; results can be seen in Table II below. The Fisher score [15] approach was used to rank and select the most information features in the datasets. <br />
<br />
[[File:Table_II_kNN.png | center | 800x800px]]<br />
<br />
From Table II, the proposed kTree and k*Tree approaches outperformed kNN, AD-kNN, FASBIR and LC-KNN when tested for varying feature numbers. The S-kNN and GS-kNN approaches remained the best in terms of classification accuracy, but were greatly outperformed in terms of running cost by k*Tree. The cause for this is that k*Tree only scans a subsample of the training samples for kNN classification, while S-kNN and GS-kNN scan all training samples.<br />
<br />
== Conclusion == <br />
<br />
This paper introduced two novel approaches for kNN classification algorithms that can determine optimal k-values for each test sample. The proposed kTree and k*Tree methods achieve efficient classification by designing a training step that reduces the run time of the test stage. Based on the experimental results for varying sample sizes and differing feature numbers, it was observed that the proposed methods outperformed existing ones in terms of running cost while still achieving similar or better classification accuracies. Future areas of investigation could focus on the improvement of kTree and k*Tree for data with large numbers of features. <br />
<br />
== Critiques == <br />
<br />
*The paper only assessed classification accuracy through cross-validation accuracy. However, it would be interesting to investigate how the proposed methods perform using different metrics, such as AUC, precision-recall curves, or in terms of holdout test data set accuracy. <br />
* The authors addressed that some of the UCI datasets contained imbalance data (such as the Climate and German data sets) while others did not. However, the nature of the class imbalance was not extreme, and the effect of imbalanced data on algorithm performance was not discussed or assessed. Moreover, it would have been interesting to see how the proposed algorithms performed on highly imbalanced datasets in conjunction with common techniques to address imbalance (e.g. oversampling, undersampling, etc.). <br />
*While the authors contrast their ktTee and k*Tree approach with different kNN methods, the paper could contrast their results with more of the approaches discussed in the Related Work section of their paper. For example, it would be interesting to see how the kTree and k*Tree results compared to Góra and Wojna varied optimal k method.<br />
<br />
* The paper conducted an experiment on kNN, AD-kNN, S-kNN, GS-kNN,FASBIR and LC-kNN with different sample sizes and feature numbers. It would be interesting to discuss why the running cost of FASBIR is between that of kTree and k*Tree in figure 21.<br />
<br />
* A different [https://iopscience.iop.org/article/10.1088/1757-899X/725/1/012133/pdf paper] also discusses optimizing the K value for the kNN algorithm in clustering. However, this paper suggests using the expectation-maximization algorithm as a means of finding the optimal k value.<br />
<br />
* It would be really helpful if Ktrees method can be explained at the very beginning. The transition from KNN to Ktrees are not very smooth.<br />
<br />
== References == <br />
<br />
[1] C. Zhang, Y. Qin, X. Zhu, and J. Zhang, “Clustering-based missing value imputation for data preprocessing,” in Proc. IEEE Int. Conf., Aug. 2006, pp. 1081–1086.<br />
<br />
[2] Y. Song, J. Huang, D. Zhou, H. Zha, and C. L. Giles, “IKNN: Informative K-nearest neighbor pattern classification,” in Knowledge Discovery in Databases. Berlin, Germany: Springer, 2007, pp. 248–264.<br />
<br />
[3] P. Vincent and Y. Bengio, “K-local hyperplane and convex distance nearest neighbor algorithms,” in Proc. NIPS, 2001, pp. 985–992.<br />
<br />
[4] V. Premachandran and R. Kakarala, “Consensus of k-NNs for robust neighborhood selection on graph-based manifolds,” in Proc. CVPR, Jun. 2013, pp. 1594–1601.<br />
<br />
[5] X. Zhu, S. Zhang, Z. Jin, Z. Zhang, and Z. Xu, “Missing value estimation for mixed-attribute data sets,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 1, pp. 110–121, Jan. 2011.<br />
<br />
[6] F. Sahigara, D. Ballabio, R. Todeschini, and V. Consonni, “Assessing the validity of QSARS for ready biodegradability of chemicals: An applicability domain perspective,” Current Comput.-Aided Drug Design, vol. 10, no. 2, pp. 137–147, 2013.<br />
<br />
[7] S. Zhang, M. Zong, K. Sun, Y. Liu, and D. Cheng, “Efficient kNN algorithm based on graph sparse reconstruction,” in Proc. ADMA, 2014, pp. 356–369.<br />
<br />
[8] X. Zhu, L. Zhang, and Z. Huang, “A sparse embedding and least variance encoding approach to hashing,” IEEE Trans. Image Process., vol. 23, no. 9, pp. 3737–3750, Sep. 2014.<br />
<br />
[9] U. Lall and A. Sharma, “A nearest neighbor bootstrap for resampling hydrologic time series,” Water Resour. Res., vol. 32, no. 3, pp. 679–693, 1996.<br />
<br />
[10] D. Cheng, S. Zhang, Z. Deng, Y. Zhu, and M. Zong, “KNN algorithm with data-driven k value,” in Proc. ADMA, 2014, pp. 499–512.<br />
<br />
[11] F. Sahigara, D. Ballabio, R. Todeschini, and V. Consonni, “Assessing the validity of QSARS for ready biodegradability of chemicals: An applicability domain perspective,” Current Comput.-Aided Drug Design, vol. 10, no. 2, pp. 137–147, 2013. <br />
<br />
[12] Z. H. Zhou and Y. Yu, “Ensembling local learners throughmultimodal perturbation,” IEEE Trans. Syst. Man, B, vol. 35, no. 4, pp. 725–735, Apr. 2005.<br />
<br />
[13] Z. H. Zhou, Ensemble Methods: Foundations and Algorithms. London, U.K.: Chapman & Hall, 2012.<br />
<br />
[14] Z. Deng, X. Zhu, D. Cheng, M. Zong, and S. Zhang, “Efficient kNN classification algorithm for big data,” Neurocomputing, vol. 195, pp. 143–148, Jun. 2016.<br />
<br />
[15] K. Tsuda, M. Kawanabe, and K.-R. Müller, “Clustering with the fisher score,” in Proc. NIPS, 2002, pp. 729–736.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Music_Recommender_System_Based_using_CRNN&diff=48458Music Recommender System Based using CRNN2020-11-30T16:38:55Z<p>Jdhchow: </p>
<hr />
<div>==Introduction and Objective:==<br />
<br />
In the digital era of music streaming, companies, such as Spotify and Pandora, are faced with the following challenge: can they provide users with relevant and personalized music recommendations amidst the ever-growing abundance of music and user data.<br />
<br />
The objective of this paper is to implement a personalized music recommender system that takes user listening history as input and continually finds new music that captures individual user preferences.<br />
<br />
This paper argues that a music recommendation system should vary from the general recommendation system used in practice since it should combine music feature recognition and audio processing technologies to extract music features, and combine them with data on user preferences.<br />
<br />
The authors of this paper took a content-based music approach to build the recommendation system - specifically, comparing the similarity of features based on the audio signal.<br />
<br />
The following two-method approach to building the recommendation system was followed:<br />
#Make recommendations including genre information extracted from classification algorithms.<br />
#Make recommendations without genre information.<br />
<br />
The authors used convolutional recurrent neural networks (CRNN), which is a combination of convolutional neural networks (CNN) and recurrent neural network(RNN), as their main classification model.<br />
<br />
==Methods and Techniques:==<br />
Generally, a music recommender can be divided into three main parts: (I) users, (ii) items, and (iii) user-item matching algorithms. First, we generated users' music tastes based on their profiles. Second, item profiling includes editorial, cultural, and acoustic metadata were collected for listeners' satisfaction. Finally, we come to the matching algorithm that suggests recommended personalized music to listeners. <br />
<br />
To classify music, the original music’s audio signal is converted into a spectrogram image. Using the image and the Short Time Fourier Transform (STFT), we convert the data into the Mel scale which is used in the CNN and CRNN models. <br />
=== Mel Scale: === <br />
The scale of pitches that are heard by listeners, which translates to equal pitch increments.<br />
<br />
[[File:Mel.png|frame|none|Mel Scale on Spectrogram]]<br />
<br />
=== Short Time Fourier Transform (STFT): ===<br />
The transformation that determines the sinusoidal frequency of the audio, with a Hanning smoothing function. In the continuous case this is written as: <math>\mathbf{STFT}\{x(t)\}(\tau,\omega) \equiv X(\tau, \omega) = \int_{-\infty}^{\infty} x(t) w(t-\tau) e^{-i \omega t} \, d t </math><br />
<br />
where: <math>w(\tau)</math> is the Hanning smoothing function<br />
<br />
=== Convolutional Neural Network (CNN): ===<br />
A Convolutional Neural Network is a Neural Network that uses convolution in place of matrix multiplication for some layer calculations. By training the data, weights for inputs are updated to find the most significant data relevant to classification. These convolutional layers gather small groups of data and with kernels, and try to find patterns that can help find features in the overall data. The features are then used for classification. Padding is also used to maintain the data on the edges. The image on the left represents the mathematical expression of a convolution operation, while the right image demonstrates an application of a kernel on the data.<br />
<br />
[[File:Convolution.png|thumb|400px|left|Convolution Operation]]<br />
[[File:PaddingKernels.png|thumb|400px|center|Example of Padding (white 0s) and Kernels (blue square)]]<br />
<br />
=== Convolutional Recurrent Neural Network (CRNN): === <br />
Similar Neural Network as CNN, with the addition of a GRU, which is a Recurrent Neural Network (RNN). An RNN is used to treat sequential data, by reusing the activation function of previous nodes to update the output. A Gated Recurrent Unit (GRU) is used to store more long-term memory and will help train the early hidden layers.<br />
<br />
[[File:GRU441.png|thumb|400px|left|Gated Recurrent Unit (GRU)]]<br />
[[File:Recurrent441.png|thumb|400px|center|Diagram of General Recurrent Neural Network]]<br />
<br />
==Data Screening:==<br />
<br />
The authors of this paper used a publicly available music dataset made up of 25,000 30-second songs from the Free Music Archives which contain 16 different genres. The data is cleaned up by removing low audio quality songs, wrong labelled genre and those that has multiple genres. To ensure a balanced dataset, only 1000 songs each from the genres of classical, electronic, folk, hip-hop, instrumental, jazz and rock were used in the final model. <br />
<br />
[[File:Data441.png|thumb|200px|none|Data sorted by music genre]]<br />
<br />
==Implementation:==<br />
<br />
=== Modeling Neural Networks ===<br />
<br />
As noted previously, both CNNs and CRNNs were used to model the data. The advantage of CRNNs is that they are able to model time sequence patterns in addition to frequency features from the spectrogram, allowing for greater identification of important features. Furthermore, feature vectors produced before the classification stage could be used to improve accuracy. <br />
<br />
In implementing the neural networks, the Mel-spectrogram data was split up into training, validation, and test sets at a ratio of 8:1:1 respectively and labelled via one-hot encoding. This made it possible for the categorical data to be labelled correctly for binary classification. As opposed to classical stochastic gradient descent, the authors opted to use Adam optimization to update weights in the training phase. Binary cross-entropy was used as the loss function. <br />
<br />
In both the CNN and CRNN models, the data was trained over 100 epochs with a binary cross-entropy loss function. Notable model specific details are below:<br />
<br />
'''CNN'''<br />
* Five convolutional layers with 3x3 kernel, stride 1, padding, batch normalization, and ReLU activation<br />
* Max pooling layers <br />
* The sigmoid function was used as the output layer<br />
<br />
'''CRNN'''<br />
* Four convolutional layers with 3x3 kernel, stride 1, padding, batch normalization, ReLU activation, and dropout rate 0.1<br />
* Max pooling layers <br />
* Two GRU layers<br />
* The sigmoid function was used as the output layer<br />
<br />
The CNN and CRNN architecture is also given in the charts below.<br />
<br />
[[File:CNN441.png|thumb|800px|none|Implementation of CNN Model]]<br />
[[File:CRNN441.png|thumb|800px|none|Implementation of CRNN Model]]<br />
<br />
=== Music Recommendation System ===<br />
<br />
The recommendation system is computed by the cosine similarity of the extraction features from the neural network. Each genre will have a song act as a centre point for each class. The final inputs of the trained neural networks will be the feature variables. The featured variables will be used in the cosine similarity to find the best recommendations. <br />
<br />
The values are between [-1,1], where larger values are songs that have similar features. When the user inputs five songs, those songs become the new inputs in the neural networks and the features are used by the cosine similarity with other music. The largest five cosine similarities are used as recommendations.<br />
[[File:Cosine441.png|frame|100px|none|Cosine Similarity]]<br />
<br />
== Evaluation Metrics ==<br />
=== Precision: ===<br />
* The proportion of True Positives with respect to the '''predicted''' positive cases (true positives and false positives)<br />
* For example, out of all the songs that the classifier '''predicted''' as Classical, how many are actually Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among those predicted to be of that certain genre<br />
<br />
=== Recall: ===<br />
* The proportion of True Positives with respect to the '''actual''' positive cases (true positives and false negatives)<br />
* For example, out of all the songs that are '''actually''' Classical, how many are correctly predicted to be Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among the correct instances of that genre<br />
<br />
=== F1-Score: ===<br />
An accuracy metric that combines the classifier’s precision and recall scores by taking the harmonic mean between the two metrics:<br />
<br />
[[File:F1441.png|frame|100px|none|F1-Score]]<br />
<br />
=== Receiver operating characteristics (ROC): ===<br />
* A graphical metric that is used to assess a classification model at different classification thresholds <br />
* In the case of a classification threshold of 0.5, this means that if <math>P(Y = k | X = x) > 0.5</math> then we classify this instance as class k<br />
* Plots the true positive rate versus false positive rate as the classification threshold is varied<br />
<br />
[[File:ROCGraph.jpg|thumb|400px|none|ROC Graph. Comparison of True Positive Rate and False Positive Rate]]<br />
<br />
=== Area Under the Curve (AUC) ===<br />
AUC is the area under the ROC in doing so, the ROC provides an aggregate measure across all possible classification thresholds.<br />
<br />
In the context of the paper: When scoring all songs as <math>Prob(Classical | X=x)</math>, it is the probability that the model ranks a random Classical song at a higher probability than a random non-Classical song.<br />
<br />
[[File:AUCGraph.jpg|thumb|400px|none|Area under the ROC curve.]]<br />
<br />
== Results ==<br />
=== Accuracy Metrics ===<br />
The table below is the accuracy metrics with the classification threshold of 0.5.<br />
<br />
[[File:TruePositiveChart.jpg|thumb|none|True Positive / False Positive Chart]]<br />
On average, CRNN outperforms CNN in true positive and false positive cases. In addition, it is very apparent that false positives are much more frequent for songs in the Instrumental genre, perhaps indicating that more pre-processing needs to be done for songs in this genre or that it should be excluded from analysis completely given how most music has instrumental components.<br />
<br />
<br />
[[File:F1Chart441.jpg|thumb|400px|none|F1 Chart]]<br />
On average, CRNN outperforms CNN in F1-score. <br />
<br />
<br />
[[File:AUCChart.jpg|thumb|400px|none|AUC Chart]]<br />
On average, CRNN also outperforms CNN in AUC metric.<br />
<br />
<br />
CRNN models that consider the frequency features and time sequence patterns of songs have a better classification performance through metrics such as F1 score and AUC when comparing to CNN classifier.<br />
<br />
=== Evaluation of Music Recommendation System: ===<br />
<br />
* A listening experiment was performed with 30 participants to access user responses to given music recommendations.<br />
* Participants choose 5 pieces of music they enjoyed and the recommender system generated 5 new recommendations. The participants then evaluated the music recommendation by recording whether the song was liked or disliked.<br />
* The recommendation system takes two approaches to the recommendation:<br />
** Method one uses only the value of cosine similarity.<br />
** Method two uses the value of cosine similarity and information on music genre.<br />
*Perform test of significance of differences in average user likes between the two methods using a t-statistic:<br />
[[File:H0441.png|frame|100px|none|Hypothesis test between method 1 and method 2]]<br />
<br />
Comparing the two methods, <math> H_0: u_1 - u_2 = 0</math>, we have <math> t_{stat} = -4.743 < -2.037 </math>, which demonstrates that the increase in average user likes with the addition of music genre information is statistically significant.<br />
<br />
== Conclusion: ==<br />
<br />
Here are two main conclusions obtained from this paper:<br />
<br />
- To increase the predictive capabilities of the music recommendation system, the music genre should be a key feature to analyze.<br />
<br />
- To extract the song genre from a song’s audio signals and get overall better performance, CRNN’s are superior to CNN’s as they consider frequency in features and time sequence patterns of audio signals. <br />
<br />
According to analyses in the paper, the authors also suggested adding other music features like tempo gram for capturing local tempo to improve the accuracy of the recommender system.<br />
<br />
== Critiques/ Insights: ==<br />
# The authors fail to give reference to the performance of current recommendation algorithms used in the industry; my critique would be for the authors to bench-mark their novel approach with other recommendation algorithms such as collaborative filtering to see if there is a lift in predictive capabilities.<br />
# The listening experiment used to evaluate the recommendation system only includes songs that are outputted by the model. Users may be biased if they believe all songs have come from a recommendation system. To remove bias, we suggest having 15 songs where 5 songs are recommended and 10 songs are set. With this in the user’s mind, it may remove some bias in response and give more accurate predictive capabilities. <br />
# They could go into more details about how CRNN makes it perform better than CNN, in terms of attributes of each network.<br />
# The methodology introduced in this paper is probably also suitable for movie recommendations. As music is presented as spectrograms (images) in a time sequence, and it is very similar to a movie. <br />
# The way of evaluation is a very interesting approach. Since it's usually not easy to evaluate the testing result when it's subjective. By listing all these evaluations' performance, the result would be more comprehensive. A practice that might reduce bias is by coming back to the participants after a couple of days and asking whether they liked the music that was recommended. Often times music "grows" on people and their opinion of a new song may change after some time has passed. <br />
# The paper lacks the comparison between the proposed algorithm and the music recommendation algorithms being used now. It will be clearer to show the superiority of this algorithm.<br />
# The GAN neural network has been proposed to enhance the performance of the neural network, so an improved result may appear after considering using GAN.<br />
# The limitation of CNN and CRNN could be that they are only able to process the spectrograms with single labels rather than multiple labels. This is far from enough for the music recommender systems in today's music industry since the edges between various genres are blurred.<br />
# according to the author, the recommender system is done by calculating the cosine similarity of extraction features from one music to another music. Is possible to represent it by Euclidean distance or p-norm distances?<br />
# In real-life application, most of the music software will have the ability to recommend music to the listener and ask do they like the music that was recommended. It would be a nice application by involving some new information from the listener.<br />
# This paper is very similar to another [https://link.springer.com/chapter/10.1007/978-3-319-46131-1_29 paper], written by Bruce Fewerda and Markus Schedl. Both papers are suggesting methods of building music recommendation systems. However, this paper recommends music based on genre, but the paper written by Fewerda and Schedl suggests a personality-based user modeling for music recommender systems.<br />
# Actual music listeners do not listen to one genre of music, and in fact listening to the same track or the same genre would be somewhat unusual. Could this method be used to make recommendations not on genre, but based on other catogories? (Such as the theme of the lyrics, the pitch of the singer, or the date published). Would this model be able to diffentiate between tracks of varying "lyric vocabulation difficulty"? Or would NLP algorithms be needed to consider lyrics?<br />
# This model can be applied to many other fields such as recommending the news in the news app, recommending things to buy in the amazon, recommending videos to watch in YOUTUBE and so on based on the user information.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Music_Recommender_System_Based_using_CRNN&diff=48457Music Recommender System Based using CRNN2020-11-30T16:37:52Z<p>Jdhchow: </p>
<hr />
<div>==Introduction and Objective:==<br />
<br />
In the digital era of music streaming, companies, such as Spotify and Pandora, are faced with the following challenge: can they provide users with relevant and personalized music recommendations amidst the ever-growing abundance of music and user data.<br />
<br />
The objective of this paper is to implement a personalized music recommender system that takes user listening history as input and continually finds new music that captures individual user preferences.<br />
<br />
This paper argues that a music recommendation system should vary from the general recommendation system used in practice since it should combine music feature recognition and audio processing technologies to extract music features, and combine them with data on user preferences.<br />
<br />
The authors of this paper took a content-based music approach to build the recommendation system - specifically, comparing the similarity of features based on the audio signal.<br />
<br />
The following two-method approach to building the recommendation system was followed:<br />
#Make recommendations including genre information extracted from classification algorithms.<br />
#Make recommendations without genre information.<br />
<br />
The authors used convolutional recurrent neural networks (CRNN), which is a combination of convolutional neural networks (CNN) and recurrent neural network(RNN), as their main classification model.<br />
<br />
==Methods and Techniques:==<br />
Generally, a music recommender can be divided into three main parts: (I) users, (ii) items, and (iii) user-item matching algorithms. First, we generated users' music tastes based on their profiles. Second, item profiling includes editorial, cultural, and acoustic metadata were collected for listeners' satisfaction. Finally, we come to the matching algorithm that suggests recommended personalized music to listeners. <br />
<br />
To classify music, the original music’s audio signal is converted into a spectrogram image. Using the image and the Short Time Fourier Transform (STFT), we convert the data into the Mel scale which is used in the CNN and CRNN models. <br />
=== Mel Scale: === <br />
The scale of pitches that are heard by listeners, which translates to equal pitch increments.<br />
<br />
[[File:Mel.png|frame|none|Mel Scale on Spectrogram]]<br />
<br />
=== Short Time Fourier Transform (STFT): ===<br />
The transformation that determines the sinusoidal frequency of the audio, with a Hanning smoothing function. In the continuous case this is written as: <math>\mathbf{STFT}\{x(t)\}(\tau,\omega) \equiv X(\tau, \omega) = \int_{-\infty}^{\infty} x(t) w(t-\tau) e^{-i \omega t} \, d t </math><br />
<br />
where: <math>w(\tau)</math> is the Hanning smoothing function<br />
<br />
=== Convolutional Neural Network (CNN): ===<br />
A Convolutional Neural Network is a Neural Network that uses convolution in place of matrix multiplication for some layer calculations. By training the data, weights for inputs are updated to find the most significant data relevant to classification. These convolutional layers gather small groups of data and with kernels, and try to find patterns that can help find features in the overall data. The features are then used for classification. Padding is also used to maintain the data on the edges. The image on the left represents the mathematical expression of a convolution operation, while the right image demonstrates an application of a kernel on the data.<br />
<br />
[[File:Convolution.png|thumb|400px|left|Convolution Operation]]<br />
[[File:PaddingKernels.png|thumb|400px|center|Example of Padding (white 0s) and Kernels (blue square)]]<br />
<br />
=== Convolutional Recurrent Neural Network (CRNN): === <br />
Similar Neural Network as CNN, with the addition of a GRU, which is a Recurrent Neural Network (RNN). An RNN is used to treat sequential data, by reusing the activation function of previous nodes to update the output. A Gated Recurrent Unit (GRU) is used to store more long-term memory and will help train the early hidden layers.<br />
<br />
[[File:GRU441.png|thumb|400px|left|Gated Recurrent Unit (GRU)]]<br />
[[File:Recurrent441.png|thumb|400px|center|Diagram of General Recurrent Neural Network]]<br />
<br />
==Data Screening:==<br />
<br />
The authors of this paper used a publicly available music dataset made up of 25,000 30-second songs from the Free Music Archives which contain 16 different genres. The data is cleaned up by removing low audio quality songs, wrong labelled genre and those that has multiple genres. To ensure a balanced dataset, only 1000 songs each from the genres of classical, electronic, folk, hip-hop, instrumental, jazz and rock were used in the final model. <br />
<br />
[[File:Data441.png|thumb|200px|none|Data sorted by music genre]]<br />
<br />
==Implementation:==<br />
<br />
=== Modeling Neural Networks ===<br />
<br />
As noted previously, both CNNs and CRNNs were used to model the data. The advantage of CRNNs is that they are able to model time sequence patterns in addition to frequency features from the spectrogram, allowing for greater identification of important features. Furthermore, feature vectors produced before the classification stage could be used to improve accuracy. <br />
<br />
In implementing the neural networks, the Mel-spectrogram data was split up into training, validation, and test sets at a ratio of 8:1:1 respectively and labelled via one-hot encoding. This made it possible for the categorical data to be labelled correctly for binary classification. As opposed to classical stochastic gradient descent, the authors opted to use Adam optimization to update weights in the training phase. Binary cross-entropy was used as the loss function. <br />
<br />
In both the CNN and CRNN models, the data was trained over 100 epochs with a binary cross-entropy loss function. Notable model specific details are below:<br />
'''CNN'''<br />
* Five convolutional layers with 3 X 3 kernel, stride 1, padding, batch normalization, and ReLU activation<br />
* Max pooling layers <br />
* The sigmoid function was used as the output layer<br />
<br />
'''CRNN'''<br />
* Four convolutional layers with 3 X 3 kernel, stride 1, padding, batch normalization, ReLU activation, and dropout rate 0.1<br />
* Max pooling layers <br />
* Two GRU layers<br />
* The sigmoid function was used as the output layer<br />
<br />
The CNN and CRNN architecture is also given in the charts below.<br />
<br />
[[File:CNN441.png|thumb|800px|none|Implementation of CNN Model]]<br />
[[File:CRNN441.png|thumb|800px|none|Implementation of CRNN Model]]<br />
<br />
=== Music Recommendation System ===<br />
<br />
The recommendation system is computed by the cosine similarity of the extraction features from the neural network. Each genre will have a song act as a centre point for each class. The final inputs of the trained neural networks will be the feature variables. The featured variables will be used in the cosine similarity to find the best recommendations. <br />
<br />
The values are between [-1,1], where larger values are songs that have similar features. When the user inputs five songs, those songs become the new inputs in the neural networks and the features are used by the cosine similarity with other music. The largest five cosine similarities are used as recommendations.<br />
[[File:Cosine441.png|frame|100px|none|Cosine Similarity]]<br />
<br />
== Evaluation Metrics ==<br />
=== Precision: ===<br />
* The proportion of True Positives with respect to the '''predicted''' positive cases (true positives and false positives)<br />
* For example, out of all the songs that the classifier '''predicted''' as Classical, how many are actually Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among those predicted to be of that certain genre<br />
<br />
=== Recall: ===<br />
* The proportion of True Positives with respect to the '''actual''' positive cases (true positives and false negatives)<br />
* For example, out of all the songs that are '''actually''' Classical, how many are correctly predicted to be Classical?<br />
* Describes the rate at which the classifier predicts the true genre of songs among the correct instances of that genre<br />
<br />
=== F1-Score: ===<br />
An accuracy metric that combines the classifier’s precision and recall scores by taking the harmonic mean between the two metrics:<br />
<br />
[[File:F1441.png|frame|100px|none|F1-Score]]<br />
<br />
=== Receiver operating characteristics (ROC): ===<br />
* A graphical metric that is used to assess a classification model at different classification thresholds <br />
* In the case of a classification threshold of 0.5, this means that if <math>P(Y = k | X = x) > 0.5</math> then we classify this instance as class k<br />
* Plots the true positive rate versus false positive rate as the classification threshold is varied<br />
<br />
[[File:ROCGraph.jpg|thumb|400px|none|ROC Graph. Comparison of True Positive Rate and False Positive Rate]]<br />
<br />
=== Area Under the Curve (AUC) ===<br />
AUC is the area under the ROC in doing so, the ROC provides an aggregate measure across all possible classification thresholds.<br />
<br />
In the context of the paper: When scoring all songs as <math>Prob(Classical | X=x)</math>, it is the probability that the model ranks a random Classical song at a higher probability than a random non-Classical song.<br />
<br />
[[File:AUCGraph.jpg|thumb|400px|none|Area under the ROC curve.]]<br />
<br />
== Results ==<br />
=== Accuracy Metrics ===<br />
The table below is the accuracy metrics with the classification threshold of 0.5.<br />
<br />
[[File:TruePositiveChart.jpg|thumb|none|True Positive / False Positive Chart]]<br />
On average, CRNN outperforms CNN in true positive and false positive cases. In addition, it is very apparent that false positives are much more frequent for songs in the Instrumental genre, perhaps indicating that more pre-processing needs to be done for songs in this genre or that it should be excluded from analysis completely given how most music has instrumental components.<br />
<br />
<br />
[[File:F1Chart441.jpg|thumb|400px|none|F1 Chart]]<br />
On average, CRNN outperforms CNN in F1-score. <br />
<br />
<br />
[[File:AUCChart.jpg|thumb|400px|none|AUC Chart]]<br />
On average, CRNN also outperforms CNN in AUC metric.<br />
<br />
<br />
CRNN models that consider the frequency features and time sequence patterns of songs have a better classification performance through metrics such as F1 score and AUC when comparing to CNN classifier.<br />
<br />
=== Evaluation of Music Recommendation System: ===<br />
<br />
* A listening experiment was performed with 30 participants to access user responses to given music recommendations.<br />
* Participants choose 5 pieces of music they enjoyed and the recommender system generated 5 new recommendations. The participants then evaluated the music recommendation by recording whether the song was liked or disliked.<br />
* The recommendation system takes two approaches to the recommendation:<br />
** Method one uses only the value of cosine similarity.<br />
** Method two uses the value of cosine similarity and information on music genre.<br />
*Perform test of significance of differences in average user likes between the two methods using a t-statistic:<br />
[[File:H0441.png|frame|100px|none|Hypothesis test between method 1 and method 2]]<br />
<br />
Comparing the two methods, <math> H_0: u_1 - u_2 = 0</math>, we have <math> t_{stat} = -4.743 < -2.037 </math>, which demonstrates that the increase in average user likes with the addition of music genre information is statistically significant.<br />
<br />
== Conclusion: ==<br />
<br />
Here are two main conclusions obtained from this paper:<br />
<br />
- To increase the predictive capabilities of the music recommendation system, the music genre should be a key feature to analyze.<br />
<br />
- To extract the song genre from a song’s audio signals and get overall better performance, CRNN’s are superior to CNN’s as they consider frequency in features and time sequence patterns of audio signals. <br />
<br />
According to analyses in the paper, the authors also suggested adding other music features like tempo gram for capturing local tempo to improve the accuracy of the recommender system.<br />
<br />
== Critiques/ Insights: ==<br />
# The authors fail to give reference to the performance of current recommendation algorithms used in the industry; my critique would be for the authors to bench-mark their novel approach with other recommendation algorithms such as collaborative filtering to see if there is a lift in predictive capabilities.<br />
# The listening experiment used to evaluate the recommendation system only includes songs that are outputted by the model. Users may be biased if they believe all songs have come from a recommendation system. To remove bias, we suggest having 15 songs where 5 songs are recommended and 10 songs are set. With this in the user’s mind, it may remove some bias in response and give more accurate predictive capabilities. <br />
# They could go into more details about how CRNN makes it perform better than CNN, in terms of attributes of each network.<br />
# The methodology introduced in this paper is probably also suitable for movie recommendations. As music is presented as spectrograms (images) in a time sequence, and it is very similar to a movie. <br />
# The way of evaluation is a very interesting approach. Since it's usually not easy to evaluate the testing result when it's subjective. By listing all these evaluations' performance, the result would be more comprehensive. A practice that might reduce bias is by coming back to the participants after a couple of days and asking whether they liked the music that was recommended. Often times music "grows" on people and their opinion of a new song may change after some time has passed. <br />
# The paper lacks the comparison between the proposed algorithm and the music recommendation algorithms being used now. It will be clearer to show the superiority of this algorithm.<br />
# The GAN neural network has been proposed to enhance the performance of the neural network, so an improved result may appear after considering using GAN.<br />
# The limitation of CNN and CRNN could be that they are only able to process the spectrograms with single labels rather than multiple labels. This is far from enough for the music recommender systems in today's music industry since the edges between various genres are blurred.<br />
# according to the author, the recommender system is done by calculating the cosine similarity of extraction features from one music to another music. Is possible to represent it by Euclidean distance or p-norm distances?<br />
# In real-life application, most of the music software will have the ability to recommend music to the listener and ask do they like the music that was recommended. It would be a nice application by involving some new information from the listener.<br />
# This paper is very similar to another [https://link.springer.com/chapter/10.1007/978-3-319-46131-1_29 paper], written by Bruce Fewerda and Markus Schedl. Both papers are suggesting methods of building music recommendation systems. However, this paper recommends music based on genre, but the paper written by Fewerda and Schedl suggests a personality-based user modeling for music recommender systems.<br />
# Actual music listeners do not listen to one genre of music, and in fact listening to the same track or the same genre would be somewhat unusual. Could this method be used to make recommendations not on genre, but based on other catogories? (Such as the theme of the lyrics, the pitch of the singer, or the date published). Would this model be able to diffentiate between tracks of varying "lyric vocabulation difficulty"? Or would NLP algorithms be needed to consider lyrics?<br />
# This model can be applied to many other fields such as recommending the news in the news app, recommending things to buy in the amazon, recommending videos to watch in YOUTUBE and so on based on the user information.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Semantic_Relation_Classification%E2%80%94%E2%80%94via_Convolution_Neural_Network&diff=47441Semantic Relation Classification——via Convolution Neural Network2020-11-28T22:06:41Z<p>Jdhchow: </p>
<hr />
<div><br />
<br />
<br />
== Presented by ==<br />
Rui Gong, Xinqi Ling, Di Ma,Xuetong Wang<br />
<br />
== Introduction ==<br />
One of the emerging trends of natural language technologies is their use for the humanities and sciences (Gbor et al., 2018). SemEval 2018 Task 7 mainly solves the problem of relation extraction and classification of two entities in the same sentence into 6 potential relations. The 6 relations are USAGE, RESULT, MODEL-FEATURE, PART WHOLE, TOPIC, and COMPARE.<br />
<br />
SemEval 2018 Task 7 extracted data from 350 scientific paper abstracts, which has 1228 and 1248 annotated sentences for two tasks, respectively. For each data, an example sentence was chosen with its right and left sentences, as well as an indicator showing whether the relation is reserved, then a prediction is made. <br />
<br />
Three models were used for the prediction: Linear Classifiers, Long Short-Term Memory(LSTM), and Convolutional Neural Networks (CNN). In the end, the prediction based on the CNN model was finally submitted since it performed the best among all models. By using the learned custom word embedding function, the research team added a variant of negative sampling, thereby improving performance and surpassing ordinary CNN.<br />
<br />
== Previous Work ==<br />
SemEval 2010 Task 8 (Hendrickx et al., 2010) explored the classification of natural language relations and studied the 9 relations between word pairs. However, it is not designed for scientific text analysis, and their challenge differs from the challenge of this paper in its generalizability; this paper’s relations are specific to ACL papers (e.g. MODEL-FEATURE), whereas the 2010 relations are more general, and might necessitate more common-sense knowledge than the 2018 relations. Xu et al. (2015a) and Santos et al. (2015) both applied CNN with negative sampling to finish task7. The 2017 SemEval Task 10 also featured relation extraction within scientific publications.<br />
<br />
== Algorithm ==<br />
<br />
[[File:CNN.png|800px|center]]<br />
<br />
This is the architecture of CNN. We first transform a sentence via Feature embeddings. Basically, we transform each sentence into continuous word embeddings:<br />
<br />
$$<br />
(e^{w_i})<br />
$$<br />
<br />
And word position embeddings:<br />
$$<br />
(e^{wp_i}): e_i = [e^{w_i}, e^{wp_i}]<br />
$$<br />
<br />
In the word embeddings, we got a vocabulary ‘V’, and we will make an embedding word matrix based on the position of the word in the vocabulary. This matrix is trainable and needs to be initialized by pre-trained embedding vectors.<br />
In the word position embeddings, we first need to input some words named ‘entities’ and they are the key for the machine to determine the sentence’s relation. During this process, if we have two entities, we will use the relative position of them in the sentence to make the<br />
embeddings. We will output two vectors and one of them keeps track of the first entity relative position in the sentence ( we will make the entity recorded as 0, the former word recorded as -1 and the next one 1, etc. ). And the same procedure for the second entity. Finally, we will get two vectors concatenated as the position embedding.<br />
<br />
<br />
After the embeddings, the model will transform the embedded sentence to a fix-sized representation of the whole sentence via the convolution layer, finally after the max-pooling to reduce the dimension of the output of the layers, we will get a score for each relation class via a linear transformation.<br />
<br />
<br />
After featurizing all words in the sentence. The sentence of length N can be expressed as a vector of length <math> N </math>, which looks like <br />
$$e=[e_{1},e_{2},\ldots,e_{N}]$$<br />
and each entry represents a token of the word. Also, to apply <br />
convolutional neural network, the subsets of features<br />
$$e_{i:i+j}=[e_{i},e_{i+1},\ldots,e_{i+j}]$$<br />
are given to a weight matrix <math> W\in\mathbb{R}^{(d^{w}+2d^{wp})\times k}</math> to <br />
produce a new feature, defiend as <br />
$$c_{i}=\text{tanh}(W\cdot e_{i:i+k-1}+bias)$$<br />
This process is applied to all subsets of features with length <math> k </math> starting <br />
from the first one. Then a mapped feature factor is produced:<br />
$$c=[c_{1},c_{2},\ldots,c_{N-k+1}]$$<br />
<br />
<br />
The max pooling operation is used, the <math> \hat{c}=max\{c\} </math> was picked.<br />
With different weight filter, different mapped feature vectors can be obtained. Finally, the original <br />
sentence <math> e </math> can be converted into a new representation <math> r_{x} </math> with a fixed length. For example, if there are 5 filters,<br />
then there are 5 features (<math> \hat{c} </math>) picked to create <math> r_{x} </math> for each <math> x </math>.<br />
<br />
Then, the score vector <br />
$$s(x)=W^{classes}r_{x}$$<br />
is obtained which represented the score for each class, given <math> x </math>'s entities' relation will be classified as <br />
the one with the highest score. The <math> W^{classes} </math> here is the model being trained.<br />
<br />
To improve the performance, “Negative Sampling" was used. Given the trained data point <br />
<math> \tilde{x} </math>, and its correct class <math> \tilde{y} </math>. Let <math> I=Y\setminus\{\tilde{y}\} </math> represent the <br />
incorrect labels for <math> x </math>. Basically, the distance between the correct score and the positive margin, and the negative <br />
distance (negative margin plus the second largest score) should be minimized. So the loss function is <br />
$$L=\log(1+e^{\gamma(m^{+}-s(x)_{y})})+\log(1+e^{\gamma(m^{-}-\mathtt{max}_{y'\in I}(s(x)_{y'}))})$$<br />
with margins <math> m_{+} </math>, <math> m_{-} </math>, and penalty scale factor <math> \gamma </math>.<br />
The whole training is based on ACL anthology corpus and there are 25,938 papers with 136,772,370 tokens in total, <br />
and 49,600 of them are unique.<br />
<br />
== Results ==<br />
In machine learning, the most important part is to tune the hyper-parameters. Unlike traditional hyper-parameter optimization, there are some<br />
modifications to the model in order to increase performance on the test set. There are 5 modifications that we can apply:<br />
<br />
'''1.''' Merged Training Sets. It combined two training sets to increase the data set<br />
size and it improves the equality between classes to get better predictions.<br />
<br />
'''2.''' Reversal Indicate Features. It added a binary feature.<br />
<br />
'''3.''' Custom ACL Embeddings. It embedded a word vector to an ACL-specific<br />
corps.<br />
<br />
'''4.''' Context words. Within the sentence, it varies in size on a context window<br />
around the entity-enclosed text.<br />
<br />
'''5.''' Ensembling. It used different early stop and random initializations to improve<br />
the predictions.<br />
<br />
These modifications performances well on the training data and they are shown<br />
in table 3.<br />
<br />
[[File:table3.PNG|center]]<br />
<br />
<br />
<br />
As we can see the best choice for this model is ensembling. Because the random initialization made the data more natural and avoided the overfit.<br />
During the training process, there are some methods such that they can only<br />
increase the score on the cross-validation test sets but hurt the performance on<br />
the overall macro-F1 score. Thus, these methods were eventually ruled out.<br />
<br />
<br />
[[File:table4.PNG|center]]<br />
<br />
There are six submissions in total. Three for each training set and the result<br />
is shown in figure 2.<br />
<br />
The best submission for training set 1.1 is the third submission which does not<br />
use a separate cross-validation dataset. Instead, a constant number of<br />
training epochs are run with cross-validation based on the training data.<br />
<br />
This compares to the other submissions in which 10% of the training data are<br />
removed to form the validation set (both with and without stratification).<br />
Predictions for these are made when the model has the highest validation accuracy. <br />
<br />
The best submission for the training set 1.2 is the submission which<br />
extracted 10% of the training data to form the validation dataset. Predictions are<br />
made when maximum accuracy is reached on the validation data.<br />
<br />
All in all, early stopping cannot always be based on the accuracy of the validation set<br />
since it cannot guarantee to get better performance on the real test set. Thus,<br />
we have to try new approaches and combine them together to see the prediction<br />
results. Also, doing stratification will certainly improve the performance of<br />
the test data.<br />
<br />
== Conclusions ==<br />
Throughout the process, linear classifiers, sequential random forest, LSTM, and CNN models are tested. Variations are applied to the models. Among all variations, vanilla CNN with negative sampling and ACL-embedding has significantly better performance than all others. Attention-based pooling, up-sampling, and data augmentation are also tested, but they barely perform positive increment on the behavior.<br />
<br />
== Critiques == <br />
<br />
- Applying this in news apps might be beneficial to improve readability by highlighting specific important sections.<br />
<br />
- In the section of previous work, the author mentioned 9 natural language relationships between the word pairs. Among them, 6 potential relationships are USAGE, RESULT, MODEL-FEATURE, PART WHOLE, TOPIC, and COMPARE. It would help the readers to better understand if all 9 relationships are listed in the summary.<br />
<br />
-This topic is interesting and this application might be helpful for some educational websites to improve their website to help readers focus on the important points. I think it will be nice to use Latex to type the equation in the sentence rather than center the equation on the next line. I think it will be interesting to discuss applying this way to other languages such as Chinese, Japanese, etc.<br />
<br />
- It would be a good idea if the authors can provide more details regarding ACL Embeddings and Context words modifications. Scores generated using these two modifications are quite close to the highest Ensembling modification generated score, which makes it a valid consideration to examine these two modifications in detail. <br />
<br />
- This paper is dealing with a similar problem as 'Neural Speed Reading Via Skim-RNN', num 19 paper summary. It will be an interesting approach to compare these two models' performance based on the same dataset.<br />
<br />
- I think it would be highly practical to implement this system as a page-rank system for search engines (such as google, bing, or other platforms like Facebook, Instagram, etc.) by finding the most prevalent information available in a search query and then matching the search to the related text which can be found on webpages. This could also be implemented in search bars on specific websites or locations as well.<br />
<br />
== References ==<br />
Diederik P Kingma and Jimmy Ba. 2014. Adam: A<br />
method for stochastic optimization. arXiv preprint<br />
arXiv:1412.6980.<br />
<br />
DragomirR. Radev, Pradeep Muthukrishnan, Vahed<br />
Qazvinian, and Amjad Abu-Jbara. 2013. The ACL<br />
anthology network corpus. Language Resources<br />
and Evaluation, pages 1–26.<br />
<br />
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey<br />
Dean. 2013a. Efficient estimation of word<br />
representations in vector space. arXiv preprint<br />
arXiv:1301.3781.<br />
<br />
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado,<br />
and Jeff Dean. 2013b. Distributed representations<br />
of words and phrases and their compositionality.<br />
In Advances in neural information processing<br />
systems, pages 3111–3119.<br />
<br />
Kata Gbor, Davide Buscaldi, Anne-Kathrin Schumann, Behrang QasemiZadeh, Hafa Zargayouna,<br />
and Thierry Charnois. 2018. Semeval-2018 task 7:Semantic relation extraction and classification in scientific papers. <br />
In Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval2018), New Orleans, LA, USA, June 2018.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:T358wang&diff=47331User:T358wang2020-11-28T18:34:57Z<p>Jdhchow: </p>
<hr />
<div><br />
== Group ==<br />
Rui Chen, Zeren Shen, Zihao Guo, Taohao Wang<br />
<br />
== Introduction ==<br />
<br />
Landmark recognition is an image retrieval task with its own specific challenges. This paper provides a new and effective method to recognize landmark images, which has been successfully applied to actual images. In this way, statues, buildings, and characteristic objects can be effectively identified.<br />
<br />
There are many difficulties encountered in the development process:<br />
<br />
'''1.''' The concept of landmarks is not strictly defined. Landmarks can take various forms including objects and buildings.<br />
<br />
'''2.''' The same landmark can be photographed from different angles. Certain angles angles may capture the interior of a building as opposed to its exterior. This could result in vastly different picture characteristics between angles. A good model should accurately identify landmarks regardless of perspective.<br />
<br />
'''3.''' The dataset is unbalanced. The majority of objects fall into the single class of "not landmarks", while relatively few images exist for each class of landmark. Hence, it will be challenging to obtain both a low false positive rate as well as a high recognition accuracy between classes of landmarks.<br />
<br />
There are also three potential problems:<br />
<br />
'''1.''' The processed data set contains a little error content, the image content is not clean and the quantity is huge.<br />
<br />
'''2.''' The algorithm for learning the training set must be fast and scalable.<br />
<br />
'''3.''' While displaying high-quality judgment landmarks, there is no image geographic information mixed.<br />
<br />
The article describes the deep convolutional neural network (CNN) architecture, loss function, training method, and inference aspects. Using this model, similar metrics to the state of the art model in the test were obtained and the inference time was found to be 15 times faster. Further, because of the efficient architecture, the system can serve in an online fashion. The results of quantitative experiments will be displayed through testing and deployment effect analysis to prove the effectiveness of the model.<br />
<br />
== Related Work ==<br />
<br />
Landmark recognition can be regarded as one of the tasks of image retrieval, and a large number of documents concentrate on image retrieval tasks. In the past two decades, the field of image retrieval has made significant progress, and the main methods can be divided into two categories. <br />
The first is a classic retrieval method using local features, a method based on local feature descriptors organized in bag-of-words(A bag of words model is defined as a simplified representation of the text information by retrieving only the significant words in a sentence or paragraph while disregarding its grammar. The bag of words approach is commonly used in classification tasks where the words are used as features in the model-training), spatial verification, Hamming embedding, and query expansion. These methods are dominant in image retrieval. Later, until the rise of deep convolutional neural networks (CNN), CNNs were used to generate global descriptors of input images.<br />
<br />
Another method is to selectively match the kernel Hamming embedding method extension. With the advent of deep convolutional neural networks, the most effective image retrieval method is based on training CNNs for specific tasks. Deep networks are very powerful for semantic feature representation, which allows us to effectively use them for landmark recognition. This method shows good results but brings additional memory and complexity costs. <br />
The DELF (DEep local feature) by Noh et al. proved promising results. This method combines the classic local feature method with deep learning. This allows us to extract local features from the input image and then use RANSAC for geometric verification. Random Sample Consensus (RANSAC) is a method to smooth data containing a significant percentage of errors, which is ideally suited for applications in automated image analysis where interpretation is based on the data generated by error-prone feature detectors. The goal of the project is to describe a method for accurate and fast large-scale landmark recognition using the advantages of deep convolutional neural networks.<br />
<br />
== Methodology ==<br />
<br />
This section will describe in detail the CNN architecture, loss function, training procedure, and inference implementation of the landmark recognition system. The figure below is an overview of the landmark recognition system.<br />
<br />
[[File:t358wang_landmark_recog_system.png |center|800px]]<br />
<br />
The landmark CNN consists of three parts: the main network, the embedding layer, and the classification layer. To obtain a CNN main network suitable for training landmark recognition model, fine-tuning is applied and several pre-trained backbones (Residual Networks) based on other similar datasets, including ResNet-50, ResNet-200, SE-ResNext-101, and Wide Residual Network (WRN-50-2), are evaluated based on inference quality and efficiency. Based on the evaluation results, WRN-50-2 is selected as the optimal backbone architecture. Fine-tuning is a very efficient technique in various computer vision applications because we can take advantage of everything the model has already learned and applied it to our specific task.<br />
<br />
[[File:t358wang_backbones.png |center|600px]]<br />
<br />
For the embedding layer, as shown in the below figure, the last fully-connected layer after the averaging pool is removed. Instead, a fully-connected 2048 <math>\times</math> 512 layer and a batch normalization are added as the embedding layer. After the batch norm, a fully-connected 512 <math>\times</math> n layer is added as the classification layer. The below figure shows the overview of the CNN architecture of the landmark recognition system.<br />
<br />
[[File:t358wang_network_arch.png |center|800px]]<br />
<br />
To effectively determine the embedding vectors for each landmark class (centroids), the network needs to be trained to have the members of each class to be as close as possible to the centroids. Several suitable loss functions are evaluated including Contrastive Loss, Arcface, and Center loss. The center loss is selected since it achieves the optimal test results and it trains a center of embeddings of each class and penalizes distances between image embeddings as well as their class centers. In addition, the center loss is a simple addition to softmax loss and is trivial to implement.<br />
<br />
When implementing the loss function, a new additional class that includes all non-landmark instances needs to be added and the center loss function needs to be modified as follows: Let n be the number of landmark classes, m be the mini-batch size, <math>x_i \in R^d</math> is the i-th embedding and <math>y_i</math> is the corresponding label where <math>y_i \in</math> {1,...,n,n+1}, n+1 is the label of the non-landmark class. Denote <math>W \in R^{d \times n}</math> as the weights of the classifier layer, <math>W_j</math> as its j-th column. Let <math>c_{y_i}</math> be the <math>y_i</math> th embeddings center from Center loss and <math>\lambda</math> be the balancing parameter of Center loss. Then the final loss function will be: <br />
<br />
[[File:t358wang_loss_function.png |center|600px]]<br />
<br />
In the training procedure, the stochastic gradient descent(SGD) will be used as the optimizer with momentum=0.9 and weight decay = 5e-3. For the center loss function, the parameter <math>\lambda</math> is set to 5e-5. Each image is resized to 256 <math>\times</math> 256 and several data augmentations are applied to the dataset including random resized crop, color jitter, and random flip. The training dataset is divided into four parts based on the geographical affiliation of cities where landmarks are located: Europe/Russia, North America/Australia/Oceania, Middle East/North Africa, and the Far East Regions. <br />
<br />
The paper introduces curriculum learning for landmark recognition, which is shown in the below figure. The algorithm is trained for 30 epochs and the learning rate <math>\alpha_1, \alpha_2, \alpha_3</math> will be reduced by a factor of 10 at the 12th epoch and 24th epoch.<br />
<br />
[[File:t358wang_algorithm1.png |center|600px]]<br />
<br />
In the inference phase, the paper introduces the term “centroids” which are embedding vectors that are calculated by averaging embeddings and are used to describe landmark classes. The calculation of centroids is significant to effectively determine whether a query image contains a landmark. The paper proposes two approaches to help the inference algorithm to calculate the centroids. First, instead of using the entire training data for each landmark, data cleaning is done to remove most of the redundant and irrelevant elements in the image. For example, if the landmark we are interested in is a palace which located on a city square, then images of a similar building on the same square are included in the data which can affect the centroids. Second, since each landmark can have different shooting angles, it is more efficient to calculate a separate centroid for each shooting angle. Hence, a hierarchical agglomerative clustering algorithm is proposed to partition training data into several valid clusters for each landmark and the set of centroids for a landmark L can be represented by <math>\mu_{l_j} = \frac{1}{|C_j|} \sum_{i \in C_j} x_i, j \in 1,...,v</math> where v is the number of valid clusters for landmark L and v=1 if there is no valid clusters for L. <br />
<br />
Once the centroids are calculated for each landmark class, the system can make decisions whether there is any landmark in an image. The query image is passed through the landmark CNN and the resulting embedding vector is compared with all centroids by dot product similarity using approximate k-nearest neighbors (AKNN). To distinguish landmark classes from non-landmark, a threshold <math>\eta</math> is set and it will be compared with the maximum similarity to determine if the image contains any landmarks.<br />
<br />
The full inference algorithm is described in the below figure.<br />
<br />
[[File:t358wang_algorithm2.png |center|600px]]<br />
<br />
We will now look at how the landmark database was created. The collection process was structured by countries, cities and landmarks. They divided the world into several regions: Europe, America, Middle East, Africa, Far East, Australia and Oceania. Within each region, cities were selected that contained a lot of significant landmarks, and some natural landmarks were filtered out as they are difficult to distinguish. Once the cities and landmarks were selected, both images and meta data was collected for each landmark.<br />
<br />
[[File:landmarkcleaning.png | center | 400px]]<br />
<br />
After forming the database, it had to be cleaned before it could be used to train the CNN. First, for each landmark any redundant images were removed. Then for each landmark 5 images were picked that had a high probability of the containing the landmark and were checked manually. The database was then cleaned by parts using the curriculum learning process. It is further described in the pseudocode above. The final database contained 11381 landmarks in 503 cities and 70 countries. With 2331784 landmark images and 900000 non-landmark images. The number of landmarks that have less than 100 images are called "rare".<br />
<br />
== Experiments and Analysis ==<br />
<br />
'''Offline test'''<br />
<br />
In order to measure the quality of the model, an offline test set was collected and manually labeled. According to the calculations, photos containing landmarks make up 1 − 3% of the total number of photos on average. This distribution was emulated in an offline test, and the geo-information and landmark references weren’t used. <br />
The results of this test are presented in the table below. Two metrics were used to measure the results of experiments: Sensitivity — the accuracy of a model on images with landmarks (also called Recall) and Specificity — the accuracy of a model on images without landmarks. Several types of DELF were evaluated, and the best results in terms of sensitivity and specificity were included in the table below. The table also contains the results of the model trained only with Softmax loss, Softmax, and Center loss. Thus, the table below reflects improvements in our approach with the addition of new elements in it.<br />
<br />
[[File:t358wang_models_eval.png |center|600px]]<br />
<br />
It’s very important to understand how a model works on “rare” landmarks due to the small amount of data for them. Therefore, the behavior of the model was examined separately on “rare” and “frequent” landmarks in the table below. The column “Part from total number” shows what percentage of landmark examples in the offline test has the corresponding type of landmarks. And we find that the sensitivity of “frequent” landmarks is much higher than “rare” landmarks.<br />
<br />
[[File:t358wang_rare_freq.png |center|600px]]<br />
<br />
Analysis of the behavior of the model in different categories of landmarks in the offline test is presented in the table below. These results show that the model can successfully work with various categories of landmarks. Predictably better results (92% of sensitivity and 99.5% of specificity) could also be obtained when the offline test with geo-information was launched on the model.<br />
<br />
[[File:t358wang_landmark_category.png |center|600px]]<br />
<br />
'''Revisited Paris dataset'''<br />
<br />
Revisited Paris dataset (RPar)[2] was also used to measure the quality of the landmark recognition approach. This dataset with Revisited Oxford (ROxf) is standard benchmarks for the comparison of image retrieval algorithms. In recognition, it is important to determine the landmark, which is contained in the query image. Images of the same landmark can have different shooting angles or taken inside/outside the building. Thus, it is reasonable to measure the quality of the model in the standard and adapt it to the task settings. That means not all classes from queries are presented in the landmark dataset. For those images containing correct landmarks but taken from different shooting angles within the building, we transferred them to the “junk” category, which does not influence the final score and makes the test markup closer to our model’s goal. Results on RPar with and without distractors in medium and hard modes are presented in the table below. <br />
<br />
<div style="text-align:center;"> '''Revisited Paris Medium''' </div><br />
[[File:t358wang_methods_eval1.png |center|600px]]<br />
<br />
<br />
<div style="text-align:center;"> '''Revisited Paris Hard''' </div><br />
[[File:t358wang_methods_eval2.png |center|600px]]<br />
<br />
== Comparison ==<br />
<br />
Recent most efficient approaches to landmark recognition are built on fine-tuned CNN. We chose to compare our method to DELF on how well each performs on recognition tasks. A brief summary is given below:<br />
<br />
[[File:t358wang_comparison.png |center|600px]]<br />
<br />
''' Offline test and timing '''<br />
<br />
Both approaches obtained similar results for image retrieval in the offline test (shown in the sensitivity&specificity table), but the proposed approach is much faster on the inference stage and more memory efficient.<br />
<br />
To be more detailed, during the inference stage, DELF needs more forward passes through CNN, has to search the entire database, and performs the RANSAC method for geometric verification. All of them make it much more time-consuming than our proposed approach. Our approach mainly uses centroids, this makes it take less time and needs to store fewer elements.<br />
<br />
== Conclusion ==<br />
<br />
In this paper we were hoping to solve some difficulties that emerge when trying to apply landmark recognition to the production level: there might not be a clean & sufficiently large database for interesting tasks, algorithms should be fast, scalable, and should aim for low FP and high accuracy.<br />
<br />
While aiming for these goals, we presented a way of cleaning landmark data. And most importantly, we introduced the usage of embeddings of deep CNN to make recognition fast and scalable, trained by curriculum learning techniques with modified versions of Center loss. Compared to the state-of-the-art methods, this approach shows similar results but is much faster and suitable for implementation on a large scale.<br />
<br />
== Critique ==<br />
The paper selected 5 images per landmark and checked them manually. That means the training process takes a long time on data cleaning and so the proposed algorithm lacks reusability. Also, since only the landmarks that are the largest and most popular were used to train the CNN, the trained model will probably be most useful in big cities instead of smaller cities with less popular landmarks.<br />
<br />
In addition, researchers often look for reliability and reproducibility. By using a private database and manually labelling it, it lends itself to an array of issues in terms of validity and integrity. Researchers who are looking for such an algorithm will not be able to sufficiently determine if the experiments do actually yield the claimed results. Also, manual labelling by those who are related to the individuals conducting this research also raises the question of conflict of interest. The primary experiment of this paper should be on a public and third-party dataset.<br />
<br />
It might be worth looking into the ability to generalize better. <br />
<br />
This is a very interesting implementation in some specific field. The paper shows a process to analyze the problem and trains the model based on deep CNN implementation. In future work, it would be some practical advice to compare the deep CNN model with other models. By comparison, we might receive a more comprehensive training model for landmark recognization.<br />
<br />
This summary has a good structure and the methodology part is very clear for readers to understand. Using some diagrams for the comparison with other methods is good for visualization for readers. Since the dataset is marked manually, so it is kind of time-consuming for training a model. So it might be interesting to discuss how the famous IT company (i.e. Google etc.) fix this problem.<br />
<br />
It would be beneficial if the authors could provide more explanations regarding the DELF method. Visualization of the differences between DELF and CNN from an algorithm and architecture perspective would be highly significant for the context of this paper.<br />
<br />
One challenge of landmark recognition is large number of classes. It would be good to see the comparison between the proposed model and other models in terms of efficiency.<br />
<br />
The scope of this paper seems to work specifically with some of the most well known landmarks in the world, and many of these landmarks are well known because they are very distinct in how they look. It would be interesting to see how well the model works when classifying different landmarks of similar type (ie, Notre Dame Cathedral vs. St. Paul's Cathedral, etc.). It would also be interesting to see how this model compares with other models in literature, or if this is unique, perhaps the authors could scale this model down to a landmark classification problem (castles, churches, parks, etc.) and compare against other models that way.<br />
<br />
Paper 25 (Loss Function Search in Facial Recognition) also utilizes the softmax loss function in feature discrimination in images. The difference between this paper and paper 25 is that this paper focuses on landmark images, whereas paper 25 is for facial recognition. Despite the slightly different application, both papers prove that importance of using the softmax loss function in feature discrimination, which is pretty neat.<br />
<br />
== References ==<br />
[1] Andrei Boiarov and Eduard Tyantov. 2019. Large Scale Landmark Recognition via Deep Metric Learning. In The 28th ACM International Conference on Information and Knowledge Management (CIKM ’19), November 3–7, 2019, Beijing, China. ACM, New York, NY, USA, 10 pages. https://arxiv.org/pdf/1908.10192.pdf 3357384.3357956<br />
<br />
[2] FilipRadenović,AhmetIscen,GiorgosTolias,YannisAvrithis,andOndřejChum.<br />
2018. Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking.<br />
arXiv preprint arXiv:1803.11285 (2018).</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Cvmustat&diff=47301User:Cvmustat2020-11-28T16:44:30Z<p>Jdhchow: </p>
<hr />
<div><br />
== Combine Convolution with Recurrent Networks for Text Classification == <br />
'''Team Members''': Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea<br />
<br />
'''Date''': Week of Nov 23 <br />
<br />
== Introduction ==<br />
<br />
<br />
Text classification is the task of assigning a set of predefined categories to natural language texts. It is a fundamental task in Natural Language Processing (NLP) with various applications such as sentiment analysis, and topic classification. A classic example involving text classification is given a set of News articles, is it possible to classify the genre or subject of each article? Text classification is useful as text data is a rich source of information, but extracting insights from it directly can be difficult and time-consuming as most text data is unstructured.[1] NLP text classification can help automatically structure and analyze text, quickly and cost-effectively, allowing for individuals to extract important features from the text easier than before. <br />
<br />
Text classification work mainly focuses on three topics: feature engineering, feature selection, and the use of different types of machine learning algorithms.<br />
1. Feature engineering, the most widely used feature is the bag of words feature. Some more complex functions are also designed, such as part-of-speech tags, noun phrases, and tree kernels.<br />
2. Feature selection aims to remove noisy features and improve classification performance. The most common feature selection method is to delete stop words.<br />
3. Machine learning algorithms usually use classifiers, such as Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM).<br />
<br />
In practice, pre-trained word embeddings and deep neural networks are used together for NLP text classification. Word embeddings are used to map the raw text data to an implicit space where the semantic relationships of the words are preserved; words with similar meaning have a similar representation. One can then feed these embeddings into deep neural networks to learn different features of the text. Convolutional neural networks can be used to determine the semantic composition of the text(the meaning), as it treats texts as a 2D matrix by concatenating the embedding of words together. It uses a 1D convolution operator to perform the feature mapping, and then conducts a 1D pooling operation over the time domain for obtaining a fixed-length output feature vector, and it is able to capture both local and position invariant features of the text.[2] Alternatively, Recurrent Neural Networks can be used to determine the contextual meaning of each word in the text (how each word relates to one another) by treating the text as sequential data and then analyzing each word separately. [3] Previous approaches to attempt to combine these two neural networks to incorporate the advantages of both models involve streamlining the two networks, which might decrease their performance. In addition, most methods incorporating a bi-directional Recurrent Neural Network usually concatenate the forward and backward hidden states at each time step, which results in a vector that does not have the interaction information between the forward and backward hidden states.[4] The hidden state in one direction contains only the contextual meaning in that particular direction, however a word's contextual representation, intuitively, is more accurate when collected and viewed from both directions. This paper argues that the failure to observe the meaning of a word in both directions causes the loss of the true meaning of the word, especially for polysemic words (words with more than one meaning) that are context-sensitive.<br />
<br />
== Paper Key Contributions ==<br />
<br />
This paper suggests an enhanced method of text classification by proposing a new way of combining Convolutional and Recurrent Neural Networks involving the addition of a neural tensor layer. The proposed method maintains each network's respective strengths that are normally lost in previous combination methods. The new suggested architecture is called CRNN, which utilizes both a CNN and RNN that run in parallel on the same input sentence. CNN uses weight matrix learning and produces a 2D matrix that shows the importance of each word based on local and position-invariant features. The bidirectional RNN produces a matrix that learns each word's contextual representation; the words' importance in relation to the rest of the sentence. A neural tensor layer is introduced on top of the RNN to obtain the fusion of bi-directional contextual information surrounding a particular word. This method combines these two matrix representations and classifies the text, providing the important information of each word for prediction, which helps to explain the results. The model also uses dropout and L2 regularization to prevent overfitting.<br />
<br />
== CRNN Results vs Benchmarks ==<br />
<br />
In order to benchmark the performance of the CRNN model, as well as compare it to other previous efforts, multiple datasets and classification problems were used. All of these datasets are publicly available and can be easily downloaded by any user for testing.<br />
<br />
- '''Movie Reviews:''' a sentiment analysis dataset, with two classes (positive and negative).<br />
<br />
- '''Yelp:''' a sentiment analysis dataset, with five classes. For this test, a subset of 120,000 reviews was randomly chosen from each class for a total of 600,000 reviews.<br />
<br />
- '''AG's News:''' a news categorization dataset, using only the 4 largest classes from the dataset.<br />
<br />
- '''20 Newsgroups:''' a news categorization dataset, again using only 4 large classes from the dataset.<br />
<br />
- '''Sogou News:''' a Chinese news categorization dataset, using the 4 largest classes from the dataset.<br />
<br />
- '''Yahoo! Answers:''' a topic classification dataset, with 10 classes.<br />
<br />
For the English language datasets, the initial word representations were created using the publicly available ''word2vec'' [https://code.google.com/p/word2vec/] from Google news. For the Chinese language dataset, ''jieba'' [https://github.com/fxsjy/jieba] was used to segment sentences, and then 50-dimensional word vectors were trained on Chinese ''wikipedia'' using ''word2vec''.<br />
<br />
A number of other models are run against the same data after preprocessing. Some of these models include:<br />
<br />
- '''Self-attentive LSTM:''' an LSTM model with multi-hop attention for sentence embedding.<br />
<br />
- '''RCNN:''' the RCNN's recurrent structure allows for increased depth of capture for contextual information. Less noise is introduced on account of the model's holistic structure (compared to local features).<br />
<br />
The following results are obtained:<br />
<br />
[[File:table of results.png|550px|center]]<br />
<br />
The bold results represent the best performing model for a given dataset. These results show that the CRNN model manages to be the best performing in 4 of the 6 datasets, with the Self-attentive LSTM beating the CRNN by 0.03 and 0.12 on the news categorization problems. Considering that the CRNN model has better performance than the Self-attentive LSTM on the other 4 datasets, this suggests that the CRNN model is a better performer overall in the conditions of this benchmark.<br />
<br />
It should be noted that including the neural tensor layer in the CRNN model leads to a significant performance boost compared to the CRNN models without it. The performance boost can be attributed to the fact that the neural tensor layer captures the surrounding contextual information for each word, and brings this information between the forward and backward RNN in a direct method. As seen in the table, this leads to a better classification accuracy across all datasets.<br />
<br />
Another important result was that the CRNN model filter size impacted performance only in the sentiment analysis datasets, as seen in the following:<br />
<br />
[[File:filter_effects.png|550px|center]]<br />
<br />
== CRNN Model Architecture ==<br />
<br />
The CRNN model is a combination of RNN and CNN. It uses CNN to compute the importance of each word in the text and utilizes a neural tensor layer to fuse forward and backward hidden states of bi-directional RNN.<br />
<br />
The input of the network is a text, which is a sequence of words. The output of the network is the text representation that is subsequently used as input of a fully-connected layer to obtain the class prediction.<br />
<br />
'''RNN Pipeline:'''<br />
<br />
The goal of the RNN pipeline is to input each word in a text, and retrieve the contextual information surrounding the word and compute the contextual representation of the word itself. This is accomplished by the use of a bi-directional RNN, such that a Neural Tensor Layer (NTL) can combine the results of the RNN to obtain the final output. RNNs are well-suited to NLP tasks because of their ability to sequentially process data such as ordered text.<br />
<br />
A RNN is similar to a feed-forward neural network, but it relies on the use of hidden states. Hidden states are layers in the neural net that produce two outputs: <math> \hat{y}_{t} </math> and <math> h_t </math>. For a time step <math> t </math>, <math> h_t </math> is fed back into the layer to compute <math> \hat{y}_{t+1} </math> and <math> h_{t+1} </math>. <br />
<br />
The pipeline will actually use a variant of RNN called GRU, short for Gated Recurrent Units. This is done to address the vanishing gradient problem which causes the network to struggle to memorize words that came earlier in the sequence. Traditional RNNs are only able to remember the most recent words in a sequence, which may be problematic since words that came at the beginning of the sequence that is important to the classification problem may be forgotten. A GRU attempts to solve this by controlling the flow of information through the network using update and reset gates. <br />
<br />
Let <math>h_{t-1} \in \mathbb{R}^m, x_t \in \mathbb{R}^d </math> be the inputs, and let <math>\mathbf{W}_z, \mathbf{W}_r, \mathbf{W}_h \in \mathbb{R}^{m \times d}, \mathbf{U}_z, \mathbf{U}_r, \mathbf{U}_h \in \mathbb{R}^{m \times m}</math> be trainable weight matrices. Then the following equations describe the update and reset gates:<br />
<br />
<br />
<math><br />
z_t = \sigma(\mathbf{W}_zx_t + \mathbf{U}_zh_{t-1}) \text{update gate} \\<br />
r_t = \sigma(\mathbf{W}_rx_t + \mathbf{U}_rh_{t-1}) \text{reset gate} \\<br />
\tilde{h}_t = \text{tanh}(\mathbf{W}_hx_t + r_t \circ \mathbf{U}_hh_{t-1}) \text{new memory} \\<br />
h_t = (1-z_t)\circ \tilde{h}_t + z_t\circ h_{t-1}<br />
</math><br />
<br />
<br />
Note that <math> \sigma, \text{tanh}, \circ </math> are all element-wise functions. The above equations do the following:<br />
<br />
<ol><br />
<li> <math>h_{t-1}</math> carries information from the previous iteration and <math>x_t</math> is the current input </li><br />
<li> the update gate <math>z_t</math> controls how much past information should be forwarded to the next hidden state </li><br />
<li> the rest gate <math>r_t</math> controls how much past information is forgotten or reset </li><br />
<li> new memory <math>\tilde{h}_t</math> contains the relevant past memory as instructed by <math>r_t</math> and current information from the input <math>x_t</math> </li><br />
<li> then <math>z_t</math> is used to control what is passed on from <math>h_{t-1}</math> and <math>(1-z_t)</math> controls the new memory that is passed on<br />
</ol><br />
<br />
We treat <math>h_0</math> and <math> h_{n+1} </math> as zero vectors in the method. Thus, each <math>h_t</math> can be computed as above to yield results for the bi-directional RNN. The resulting hidden states <math>\overrightarrow{h_t}</math> and <math>\overleftarrow{h_t}</math> contain contextual information around the <math> t</math>-th word in forward and backward directions respectively. Contrary to convention, instead of concatenating these two vectors, it is argued that the word's contextual representation is more precise when the context information from different directions is collected and fused using a neural tensor layer as it permits greater interactions among each element of hidden states. Using these two vectors as input to the neural tensor layer, <math>V^i </math>, we compute a new representation that aggregates meanings from the forward and backward hidden states more accurately as follows:<br />
<br />
<math> <br />
[\hat{h_t}]_i = tanh(\overrightarrow{h_t}V^i\overleftarrow{h_t} + b_i) <br />
</math><br />
<br />
Where <math>V^i \in \mathbb{R}^{m \times m} </math> is the learned tensor layer, and <math> b_i \in \mathbb{R} </math> is the bias.We repeat this <math> m </math> times with different <math>V^i </math> matrices and <math> b_i </math> vectors. Through the neural tensor layer, each element in <math> [\hat{h_t}]_i </math> can be viewed as a different type of intersection between the forward and backward hidden states. In the model, <math> [\hat{h_t}]_i </math> will have the same size as the forward and backward hidden states. We then concatenate the three hidden states vectors to form a new vector that summarizes the context information :<br />
<math><br />
\overleftrightarrow{h_t} = [\overrightarrow{h_t}^T,\overleftarrow{h_t}^T,\hat{h_t}]^T <br />
</math><br />
<br />
We calculate this vector for every word in the text and then stack them all into matrix <math> H </math> with shape <math>n</math>-by-<math>3m</math>.<br />
<br />
<math><br />
H = [\overleftrightarrow{h_1};...\overleftrightarrow{h_n}]<br />
</math><br />
<br />
This <math>H</math> matrix is then forwarded as the results from the Recurrent Neural Network.<br />
<br />
<br />
'''CNN Pipeline:'''<br />
<br />
The goal of the CNN pipeline is to learn the relative importance of words in an input sequence based on different aspects. The process of this CNN pipeline is summarized as the following steps:<br />
<br />
<ol><br />
<li> Given a sequence of words, each word is converted into a word vector using the word2vec algorithm which gives matrix X. <br />
</li><br />
<br />
<li> Word vectors are then convolved through the temporal dimension with filters of various sizes (ie. different K) with learnable weights to capture various numerical K-gram representations. These K-gram representations are stored in matrix C.<br />
</li><br />
<br />
<ul><br />
<li> The convolution makes this process capture local and position-invariant features. Local means the K words are contiguous. Position-invariant means K contiguous words at any position are detected in this case via convolution.<br />
<br />
<li> Temporal dimension example: convolve words from 1 to K, then convolve words 2 to K+1, etc<br />
</li><br />
</ul><br />
<br />
<li> Since not all K-gram representations are equally meaningful, there is a learnable matrix W which takes the linear combination of K-gram representations to more heavily weigh the more important K-gram representations for the classification task.<br />
</li><br />
<br />
<li> Each linear combination of the K-gram representations gives the relative word importance based on the aspect that the linear combination encodes.<br />
</li><br />
<br />
<li> The relative word importance vs aspect gives rise to an interpretable attention matrix A, where each element says the relative importance of a specific word for a specific aspect.<br />
</li><br />
<br />
</ol><br />
<br />
[[File:Group12_Figure1.png |center]]<br />
<br />
<div align="center">Figure 1: The architecture of CRNN.</div><br />
<br />
== Merging RNN & CNN Pipeline Outputs ==<br />
<br />
The results from both the RNN and CNN pipeline can be merged by simply multiplying the output matrices. That is, we compute <math>S=A^TH</math> which has shape <math>z \times 3m</math> and is essentially a linear combination of the hidden states. The concatenated rows of S results in a vector in <math>\mathbb{R}^{3zm}</math>, and can be passed to a fully connected Softmax layer to output a vector of probabilities for our classification task. <br />
<br />
To train the model, we make the following decisions:<br />
<ul><br />
<li> Use cross-entropy loss as the loss function (A cross-entropy loss function usually takes in two distributions, a true distribution p and an estimated distribution q, and measures the average number of bits need to identify an event. This calculation is independent of the kind of layers used in the network as well as the kind of activation being implemented.) </li><br />
<li> Perform dropout on random columns in matrix C in the CNN pipeline </li><br />
<li> Perform L2 regularization on all parameters </li><br />
<li> Use stochastic gradient descent with a learning rate of 0.001 </li><br />
</ul><br />
<br />
== Interpreting Learned CRNN Weights ==<br />
<br />
Recall that attention matrix A essentially stores the relative importance of every word in the input sequence for every aspect chosen. Naturally, this means that A is an n-by-z matrix, with n being the number of words in the input sequence and z being the number of aspects considered in the classification task. <br />
<br />
Furthermore, for any specific aspect, words with higher attention values are more important relative to other words in the same input sequence. likewise, for any specific word, aspects with higher attention values prioritize the specific word more than other aspects.<br />
<br />
For example, in this paper, a sentence is sampled from the Movie Reviews dataset, and the transpose of attention matrix A is visualized. Each word represents an element in matrix A, the intensity of red represents the magnitude of an attention value in A, and each sentence is the relative importance of each word for a specific context. In the first row, the words are weighted in terms of a positive aspect, in the last row, the words are weighted in terms of a negative aspect, and in the middle row, the words are weighted in terms of a positive and negative aspect. Notice how the relative importance of words is a function of the aspect.<br />
<br />
[[File:Interpretation example.png|800px|center]]<br />
<br />
From the above sample, it is interesting that the word "but" is viewed as a negative aspect. From a linguistic perspective, the semantic of "but" is incredibly difficult to capture because of the degree of contextual information it needs. In this case, "but" is in the middle of a transition from a negative to a positive so the first row should also have given attention that word. Also, it seems that the model has learned to give very high attention to the two words directly adjacent to the word of high attention: "is" and "and" beside "powerful", and "an" and "cast" beside "unwieldy".<br />
<br />
== Conclusion & Summary ==<br />
<br />
This paper proposed a new architecture, the Convolutional Recurrent Neural Network, for text classification. The Convolutional Neural Network is used to learn the relative importance of each word from different aspects and stores it into a weight matrix. The Recurrent Neural Network learns each word's contextual representation through the combination of the forward and backward context information that is fused using a neural tensor layer and is stored as a matrix. These two matrices are then combined to get the text representation used for classification. Although the specifics of the performed tests are lacking, the experiment's results indicate that the proposed method performed well in comparison to most previous methods. In addition to performing well, the proposed method also provides insight into which words contribute greatly to the classification decision as the learned matrix from the Convolutional Neural Network stores the relative importance of each word. This information can then be used in other applications or analyses. In the future, one can explore the features extracted from the model and use them to potentially learn new methods such as model space. [5]<br />
<br />
== Critiques ==<br />
<br />
In the '''Method''' section of the paper, some explanations used the same notation for multiple different elements of the model. This made the paper harder to follow and understand since they were referring to different elements by identical notation.<br />
<br />
In '''Comparison of Methods''', the authors discuss the range of hyperparameter settings that they search through. While some of the hyperparameters have a large range of search values, three parameters are fixed without much explanation as to why for all experiments, size of the hidden state of GRU, number of layers, and dropout. These parameters have a lot to do with the complexity of the model and this paper could be improved by providing relevant reasoning behind these values, or by providing additional experimental results over different values of these parameters.<br />
<br />
In the '''Results''' section of the paper, they tried to show that the classification results from the CRNN model can be better interpreted than other models. In these explanations, the details were lacking and the authors did not adequately demonstrate how their model is better than others.<br />
<br />
Finally, in the '''Results''' section again, the paper compares the CRNN model to several models which they did not implement and reproduce results with. This can be seen in the chart of results above, where several models do not have entries in the table for all six datasets. Since the authors used a subset of the datasets, these other models which were not reproduced could have different accuracy scores if they had been tested on the same data as the CRNN model. This difference in training and testing data is not mentioned in the paper, and the conclusion that the CRNN model is better in all cases may not be valid.<br />
<br />
- Could this be applied to hieroglyphs to decipher/better understand them?<br />
<br />
It would be interesting to see how the attention matrix is being constructed and how attention values are being determined in each matrix. For instance, does every different subject have its own attention matrix? If so, how will the situation be handled when the same attention matrix is used in different settings?<br />
<br />
-This is an interesting topic. I think it will be better to show more results by using this method. Maybe it will be better to put the result part after the architecture part? Writing a motivation will be better since it will catch readers' "eyes". I think it will be interesting to ask: whether can we apply this to ancient Chinese poetry? Since there are lots of types of ancient Chinese poetry, doing a classification for them will be interesting.<br />
<br />
This is an interesting method, I would be curious to see if this can be combined or compared with Quasi-Recurrent Neural Networks (https://arxiv.org/abs/1611.01576). In my experience, QRNNs perform similarly to LSTMs while running significantly faster using convolutions with a special temporal pooling. This seems compatible with the neural tensor layer proposed in this paper, which may be combined to yield stronger performance with faster runtimes.<br />
<br />
== References ==<br />
----<br />
<br />
[1] Grimes, Seth. “Unstructured Data and the 80 Percent Rule.” Breakthrough Analysis, 1 Aug. 2008, breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/.<br />
<br />
[2] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A convolutional neural network for modeling sentences,”<br />
arXiv preprint arXiv:1404.2188, 2014.<br />
<br />
[3] K. Cho, B. V. Merri¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning<br />
phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv preprint<br />
arXiv:1406.1078, 2014.<br />
<br />
[4] S. Lai, L. Xu, K. Liu, and J. Zhao, “Recurrent convolutional neural networks for text classification,” in Proceedings<br />
of AAAI, 2015, pp. 2267–2273.<br />
<br />
[5] H. Chen, P. Tio, A. Rodan, and X. Yao, “Learning in the model space for cognitive fault diagnosis,” IEEE<br />
Transactions on Neural Networks and Learning Systems, vol. 25, no. 1, pp. 124–136, 2014.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:J46hou&diff=47298User:J46hou2020-11-28T16:23:54Z<p>Jdhchow: </p>
<hr />
<div>DROCC: Deep Robust One-Class Classification<br />
== Presented by == <br />
Jinjiang Lian, Yisheng Zhu, Jiawen Hou, Mingzhe Huang<br />
== Introduction ==<br />
In this paper, the “one-class” classification, whose goal is to obtain accurate discriminators for a special class, has been studied. Popular uses of this technique include anomaly detection which is widely used in detecting outliers. Anomaly detection is a well-studied area of research; however, the conventional approach of modeling with typical data using a simple function falls short when it comes to complex domains such as vision or speech. Another case where this would be useful is when recognizing “wake-word” while waking up AI systems such as Alexa. <br />
<br />
Deep learning based on anomaly detection methods attempts to learn features automatically but has some limitations. One approach is based on extending the classical data modeling techniques over the learned representations, but in this case, all the points may be mapped to a single point which makes the layer looks "perfect". The second approach is based on learning the salient geometric structure of data and training the discriminator to predict applied transformation. The result could be considered anomalous if the discriminator fails to predict the transform accurately.<br />
<br />
Thus, in this paper, a new approach called Deep Robust One-Class Classification (DROCC) was presented to solve the above concerns. DROCC is based on the assumption that the points from the class of interest lie on a well-sampled, locally linear low dimensional manifold. More specifically, we are presenting DROCC-LF which is an outlier-exposure style extension of DROCC. This extension combines the DROCC's anomaly detection loss with standard classification loss over the negative data and exploits the negative examples to learn a Mahalanobis distance.<br />
<br />
== Previous Work ==<br />
Traditional approaches for one-class problems include one-class SVM (Scholkopf et al., 1999) and Isolation Forest (Liu et al., 2008)[9]. One drawback of these approaches is that they involve careful feature engineering when applied to structured domains like images. Current state of the art methodologies to tackle these kind of problems are: <br />
<br />
1. Approach based on prediction transformations (Golan & El-Yaniv, 2018; Hendrycks et al.,2019a) [1] This approach has some shortcomings in the sense that it depends heavily on an appropriate domain-specific set of transformations that are in general hard to obtain. <br />
<br />
2. Approach of minimizing a classical one-class loss on the learned final layer representations such as DeepSVDD. (Ruff et al.,2018)[2] This method suffers from the fundamental drawback of representation collapse where the model is no longer being able to accurately recognize the feature representations.<br />
<br />
3. Approach based on balancing unbalanced training data sets using methods such as SMOTE to synthetically create outlier data to train models on.<br />
<br />
== Motivation ==<br />
Anomaly detection is a well-studied problem with a large body of research (Aggarwal, 2016; Chandola et al., 2009) [3]. The goal is to identify the outliers - the points not following a typical distribution. <br />
[[File:abnormal.jpeg | thumb | center | 1000px | Abnormal Data (Data Driven Investor, 2020)]]<br />
Classical approaches for anomaly detection are based on modeling the typical data using simple functions over the low-dimensional subspace or a tree-structured partition of the input space to detect anomalies (Sch¨olkopf et al., 1999; Liu et al., 2008; Lakhina et al., 2004) [4], such as constructing a minimum-enclosing ball around the typical data points (Tax & Duin, 2004) [5]. They broadly fall into three categories: AD via generative modeling, Deep Once Class SVM, Transformations based methods, and Side-information based AD. While these techniques are well-suited when the input is featured appropriately, they struggle on complex domains like vision and speech, where hand-designing features are difficult.<br />
<br />
'''AD via Generative Modeling:''' involves deep autoencoders and GAN based methods and have been deeply studied. But, this method solves a much harder problem then required and reconstructs the entire input during the decoding step<br />
<br />
'''Deep Once Class SVM:''' was the first method to introduce deep one-class classification for the purpose of anomaly detection, but is impeded by representation collapse.<br />
<br />
'''Transformations based methods:''' Are more recent methods that is based on self-supervised training. The training process of these methods apply transformations to the regular points and training then train the classifier to identify the transformations used. The model relies on the assumption that a point is normal iff the transformations applied to the point can be identified. Some proposed transformations are as simple as rotations and flips, or can be handcrafted and much more complicated. The various transformations that have been proposed are heavily domain dependent and are hard to design.<br />
<br />
'''Side-information based AD:''' incorporate labelled anomalous data or out-of-distribution samples. DROCC makes no assumptions regarding access to side-information.<br />
<br />
Another related problem is the one-class classification under limited negatives (OCLN). In this case, only a few negative samples are available. The goal is to find a classifier that would not misfire close negatives so that the false positive rate will be low. <br />
<br />
DROCC is robust to representation collapse by involving a discriminative component that is general and empirically accurate on most standard domains like tabular, time-series and vision without requiring any additional side information. DROCC is motivated by the key observation that generally, the typical data lies on a low-dimensional manifold, which is well-sampled in the training data. This is believed to be true even in complex domains such as vision, speech, and natural language (Pless & Souvenir, 2009). [6]<br />
<br />
== Model Explanation ==<br />
[[File:drocc_f1.jpg | center]]<br />
<div align="center">'''Figure 1'''</div><br />
<br />
(a): A normal data manifold with red dots representing generated anomalous points in Ni(r). <br />
<br />
(b): Decision boundary learned by DROCC when applied to the data from (a). Blue represents points classified as normal and red points are classified as abnormal. We observe from here that DROCC is able to capture the manifold accurately; whereas the classical methods OC-SVM and DeepSVDD perform poorly as they both try to learn a minimum enclosing ball for the whole set of positive data points. <br />
<br />
(c), (d): First two dimensions of the decision boundary of DROCC and DROCC–LF, when applied to noisy data (Section 5.2). DROCC–LF is nearly optimal while DROCC’s decision boundary is inaccurate. Yellow color sine wave depicts the train data.<br />
<br />
== DROCC ==<br />
The model is based on the assumption that the true data lies on a manifold. As manifolds resemble Euclidean space locally, our discriminative component is based on classifying a point as anomalous if it is outside the union of small L2 norm balls around the training typical points (See Figure 1a, 1b for an illustration). Importantly, the above definition allows us to synthetically generate anomalous points, and we adaptively generate the most effective anomalous points while training via a gradient ascent phase reminiscent of adversarial training. In other words, DROCC has a gradient ascent phase to adaptively add anomalous points to our training set and a gradient descent phase to minimize the classification loss by learning a representation and a classifier on top of the representations to separate typical points from the generated anomalous points. In this way, DROCC automatically learns an appropriate representation (like DeepSVDD) but is robust to a representation collapse as mapping all points to the same value would lead to poor discrimination between normal points and the generated anomalous points.<br />
<br />
The algorithm that was used to train the model is laid out below in pseudocode.<br />
<center><br />
[[File:DROCCtrain.png]]<br />
</center><br />
<br />
== DROCC-LF ==<br />
To especially tackle problems such as anomaly detection and outlier exposure (Hendrycks et al., 2019a) [7], DROCC–LF, an outlier-exposure style extension of DROCC was proposed. Intuitively, DROCC–LF combines DROCC’s anomaly detection loss (that is over only the positive data points) with standard classification loss over the negative data. In addition, DROCC–LF exploits the negative examples to learn a Mahalanobis distance to compare points over the manifold instead of using the standard Euclidean distance, which can be inaccurate for high-dimensional data with relatively fewer samples. (See Figure 1c, 1d for illustration)<br />
<br />
== Popular Dataset Benchmark Result ==<br />
<br />
[[File:drocc_auc.jpg | center]]<br />
<div align="center">'''Figure 2: AUC result'''</div><br />
<br />
The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. The average AUC (with standard deviation) for one-vs-all anomaly detection on CIFAR-10 is shown in table 1. DROCC outperforms baselines on most classes, with gains as high as 20%, and notably, nearest neighbors (NN) beats all the baselines on 2 classes.<br />
<br />
[[File:drocc_f1score.jpg | center]]<br />
<div align="center">'''Figure 3: F1-Score'''</div><br />
<br />
Table 2 shows F1-Score (with standard deviation) for one-vs-all anomaly detection on Thyroid, Arrhythmia, and Abalone datasets from the UCI Machine Learning Repository. DROCC outperforms the baselines on all three datasets by a minimum of 0.07 which is about an 11.5% performance increase.<br />
Results on One-class Classification with Limited Negatives (OCLN): <br />
[[File:ocln.jpg | center]]<br />
<div align="center">'''Figure 4: Sample positives, negatives and close negatives for MNIST digit 0 vs 1 experiment (OCLN).'''</div><br />
MNIST 0 vs. 1 Classification: <br />
We consider an experimental setup on the MNIST dataset, where the training data consists of Digit 0, the normal class, and Digit 1 as the anomaly. During the evaluation, in addition to samples from training distribution, we also have half zeros, which act as challenging OOD points (close negatives). These half zeros are generated by randomly masking 50% of the pixels (Figure 2). BCE performs poorly, with a recall of 54% only at a fixed FPR of 3%. DROCC–OE gives a recall value of 98:16% outperforming DeepSAD by a margin of 7%, which gives a recall value of 90:91%. DROCC–LF provides further improvement with a recall of 99:4% at 3% FPR. <br />
<br />
[[File:ocln_2.jpg | center]]<br />
<div align="center">'''Figure 5: OCLN on Audio Commands.'''</div><br />
Wake word Detection: <br />
Finally, we evaluate DROCC–LF on the practical problem of wake word detection with low FPR against arbitrary OOD negatives. To this end, we identify a keyword, say “Marvin” from the audio commands dataset (Warden, 2018) [8] as the positive class, and the remaining 34 keywords are labeled as the negative class. For training, we sample points uniformly at random from the above-mentioned dataset. However, for evaluation, we sample positives from the train distribution, but negatives contain a few challenging OOD points as well. Sampling challenging negatives itself is a hard task and is the key motivating reason for studying the problem. So, we manually list close-by keywords to Marvin such as Mar, Vin, Marvelous, etc. We then generate audio snippets for these keywords via a speech synthesis tool 2 with a variety of accents.<br />
Figure 3 shows that for 3% and 5% FPR settings, DROCC–LF is significantly more accurate than the baselines. For example, with FPR=3%, DROCC–LF is 10% more accurate than the baselines. We repeated the same experiment with the keyword: Seven, and observed a similar trend. In summary, DROCC–LF is able to generalize well against negatives that are “close” to the true positives even when such negatives were not supplied with the training data.<br />
<br />
== Conclusion and Future Work ==<br />
We introduced DROCC method for deep anomaly detection. It models normal data points using a low-dimensional manifold and hence can compare close points via Euclidean distance. Based on this intuition, DROCC’s optimization is formulated as a saddle point problem which is solved via a standard gradient descent-ascent algorithm. We then extended DROCC to OCLN problem where the goal is to generalize well against arbitrary negatives, assuming the positive class is well sampled and a small number of negative points are also available. Both the methods perform significantly better than strong baselines, in their respective problem settings. <br />
<br />
For computational efficiency, we simplified the projection set of both methods which can perhaps slow down the convergence of the two methods. Designing optimization algorithms that can work with the stricter set is an exciting research direction. Further, we would also like to rigorously analyze DROCC, assuming enough samples from a low-curvature manifold. Finally, as OCLN is an exciting problem that routinely comes up in a variety of real-world applications, we would like to apply DROCC–LF to a few high impact scenarios.<br />
<br />
The results of this study showed that DROCC is comparatively better for anomaly detection across many different areas, such as tabular data, images, audio, and time series, when compared to existing state-of-the-art techniques.<br />
<br />
It would be interesting to see how the DROCC method performs in situations where the anomaly is very rare, say detecting signals of volcanic explosion from seismic activity data. Such challenging anomalous situations will be a test of endurance for this method and can even help advance work in this area.<br />
<br />
== References ==<br />
[1]: Golan, I. and El-Yaniv, R. Deep anomaly detection using geometric transformations. In Advances in Neural Information Processing Systems (NeurIPS), 2018.<br />
<br />
[2]: Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., M¨uller, E., and Kloft, M. Deep one-class classification. In International Conference on Machine Learning (ICML), 2018.<br />
<br />
[3]: Aggarwal, C. C. Outlier Analysis. Springer Publishing Company, Incorporated, 2nd edition, 2016. ISBN 3319475770.<br />
<br />
[4]: Sch¨olkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., and Platt, J. Support vector method for novelty detection. In Proceedings of the 12th International Conference on Neural Information Processing Systems, 1999.<br />
<br />
[5]: Tax, D. M. and Duin, R. P. Support vector data description. Machine Learning, 54(1), 2004.<br />
<br />
[6]: Pless, R. and Souvenir, R. A survey of manifold learning for images. IPSJ Transactions on Computer Vision and Applications, 1, 2009.<br />
<br />
[7]: Hendrycks, D., Mazeika, M., and Dietterich, T. Deep anomaly detection with outlier exposure. In International Conference on Learning Representations (ICLR), 2019a.<br />
<br />
[8]: Warden, P. Speech commands: A dataset for limited vocabulary speech recognition, 2018. URL https: //arxiv.org/abs/1804.03209.<br />
<br />
[9]: Liu, F. T., Ting, K. M., and Zhou, Z.-H. Isolation forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, 2008.<br />
<br />
== Critiques/Insights ==<br />
<br />
1. It would be interesting to see this implemented in self-driving cars, for instance, to detect unusual road conditions.<br />
<br />
2. Figure 1 shows a good representation on how this model works. However, how can we know that this model is not prone to overfitting? There are many situations where there are valid points that lie outside of the line, especially new data that the model has never see before. An explanation on how this is avoided would be good.<br />
<br />
3.In the introduction part, it should first explain what is "one class", and then make a detailed application. Moreover, special definition words are used in many places in the text. No detailed explanation was given. In the end, the future application fields of DROCC and the research direction of the group can be explained.<br />
<br />
4. The geometry of this technique (classification based on incidence outside of a ball centred at a known point <math>x</math>) sounds quite similar to K-nearest neighbors. While the authors compared DROCC to single-nearest neighbor classification, choosing a higher K would result in a stronger, more regularized model. It would be interesting to see how DROCC compares to the general KNN classifier<br />
<br />
5. This is a nice summary and the authors introduce clearly on the performance of DROCC. It is nice to use Alexa as an example to catch readers' attention. I think it will be nice to include the algorithm of the DROCC or the architecture of DROCC in this summary to help us know the whole view of this method. Maybe it will be interesting to apply DROCC in biomedical studies? since one-class classification is often used in biomedical studies.<br />
<br />
6. The training method resembles adversarial learning with gradient ascent, however, there is no evaluation of this method on adversarial examples. This is quite unusual considering the paper proposed a method for robust one-class classification, and can be a security threat in real life in critical applications.<br />
<br />
7. The underlying idea behind OCLN is very similar to how neural networks are implemented in recommender systems and trained over positive/negative triplet models. In that case as well, due to the nature of implicit and explicit feedback, positive data tends to dominate the system. It would be interesting to see if insights from that area could be used to further boost the model presented in this paper.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44984Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:30:02Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j \in N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_{ij} = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}.</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}.</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification (SBIC) ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) \approx \prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy,<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44983Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:29:19Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j \in N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_{ij} = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}.</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}.</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification (SBIC) ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) \approx \prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44982Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:28:30Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j \in N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_{ij} = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}.</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}.</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification (SBIC) ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) \approx \prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44981Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:27:40Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j \in N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_{ij} = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}.</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}.</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) </math> as <math>\prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44980Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:26:56Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j \in N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_{ij} = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) </math> as <math>\prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44979Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:26:33Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j \in N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_ij = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) </math> as <math>\prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44978Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:26:15Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_{ij}}</math> where <math>j</math> in <math>N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_ij = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_{ij} = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) </math> as <math>\prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=44977stat441F212020-11-16T21:18:40Z<p>Jdhchow: </p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
[https://www.youtube.com/watch?v=TVLpSFYgF0c&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Influenza_Forecasting_Framework_based_on_Gaussian_Processes Summary]|| [https://www.youtube.com/watch?v=HZG9RAHhpXc&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Gtompkin Summary] || [https://learn.uwaterloo.ca/d2l/ext/rp/577051/lti/framedlaunch/6ec1ebea-5547-46a2-9e4f-e3dc9d79fd54]<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification Summary] || [https://www.youtube.com/watch?v=UgVRzi9Ubws]<br />
|-<br />
|Week of Nov 16 || Matthew Hall, Johnathan Chalaturnyk || 5|| Neural Ordinary Differential Equations || [https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_ODEs Summary]||<br />
|-<br />
|Week of Nov 16 || Luwen Chang, Qingyang Yu, Tao Kong, Tianrong Sun || 6|| Adversarial Attacks on Copyright Detection Systems || Paper [https://proceedings.icml.cc/static/paper_files/icml/2020/1894-Paper.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Attacks_on_Copyright_Detection_Systems Summary] ||<br />
|-<br />
|Week of Nov 16 || Casey De Vera, Solaiman Jawad, Jihoon Han || 7|| IPBoost – Non-Convex Boosting via Integer Programming || [https://arxiv.org/pdf/2002.04679.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || Yuxin Wang, Evan Peters, Yifan Mou, Sangeeth Kalaichanthiran || 8|| What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games || [https://arxiv.org/pdf/1805.02777.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=what_game_are_we_playing Summary] || [https://www.youtube.com/watch?v=9qJoVxo3hnI&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Yuchuan Wu || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || Zhou Zeping, Siqi Li, Yuqin Fang, Fu Rao || 10|| A survey of neural network-based cancer prediction models from microarray data || [https://www.sciencedirect.com/science/article/pii/S0933365717305067] || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:J46hou Summary] ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Networks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || Taohao Wang, Zeren Shen, Zihao Guo, Rui Chen || 13|| Deep multiple instance learning for image classification and auto-annotation || [https://jiajunwu.com/papers/dmil_cvpr.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Task_Understanding_from_Confusing_Multi-task_Data Summary] ||<br />
|-<br />
|Week of Nov 23 || Rui Gong, Xuetong Wang, Xinqi Ling, Di Ma || 15|| Semantic Relation Classification via Convolution Neural Network|| [https://www.aclweb.org/anthology/S18-1127.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| Graph Structure of Neural Networks || [https://proceedings.icml.cc/paper/2020/file/757b505cfd34c64c85ca5b5690ee5293-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title= Summary] ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Superhuman AI for multiplayer poker || [https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || Jerry Huang, Daniel Jiang, Minyan Dai || 19|| Neural Speed Reading Via Skim-RNN ||[https://arxiv.org/pdf/1711.02085.pdf?fbclid=IwAR3EeFsKM_b5p9Ox7X9mH-1oI3U3oOKPBy3xUOBN0XvJa7QW2ZeJJ9ypQVo Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_Speed_Reading_via_Skim-RNN Summary]||<br />
|-<br />
|Week of Nov 23 ||Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew || 20|| DivideMix: Learning with Noisy Labels as Semi-supervised Learning || [https://openreview.net/pdf?id=HJgExaVtwr] || ||<br />
|-<br />
|Week of Nov 30 || Banno Dion, Battista Joseph, Kahn Solomon || 21|| Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks || [https://www.sciencedirect.com/science/article/pii/S1877050919310646] || ||<br />
|-<br />
|Week of Nov 30 || Sai Arvind Budaraju, Isaac Ellmen, Dorsa Mohammadrezaei, Emilee Carson || 22|| A universal SNP and small-indel variant caller using deep neural networks||[https://www.nature.com/articles/nbt.4235.epdf?author_access_token=q4ZmzqvvcGBqTuKyKgYrQ9RgN0jAjWel9jnR3ZoTv0NuM3saQzpZk8yexjfPUhdFj4zyaA4Yvq0LWBoCYQ4B9vqPuv8e2HHy4vShDgEs8YxI_hLs9ov6Y1f_4fyS7kGZ Paper] || ||<br />
|-<br />
|Week of Nov 30 || Daniel Fagan, Cooper Brooke, Maya Perelman || 23|| Efficient kNN Classification With Different Number of Nearest Neighbors || [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898482 Paper] || ||<br />
|-<br />
|Week of Nov 30 || Karam Abuaisha, Evan Li, Jason Pu, Nicholas Vadivelu || 24|| Being Bayesian about Categorical Probability || [https://proceedings.icml.cc/static/paper_files/icml/2020/3560-Paper.pdf Paper] || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 30 ||Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee || 26|| Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms || [https://arxiv.org/pdf/1912.07618.pdf?fbclid=IwAR0RwATSn4CiT3qD9LuywYAbJVw8YB3nbex8Kl19OCExIa4jzWaUut3oVB0 Paper] || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&fbclid=IwAR1Tad2DAM7LT6NXXuSYDZtHHBvN0mjZtDdCOiUFFq_XwVcQxG3hU-3XcaE] ||<br />
|-<br />
|Week of Nov 30 || Stan Lee, Seokho Lim, Kyle Jung, Daehyun Kim || 27|| Bag of Tricks for Efficient Text Classification || [https://arxiv.org/pdf/1607.01759.pdf paper] || ||<br />
|-<br />
|Week of Nov 30 || Yawen Wang, Danmeng Cui, ZiJie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid || 28|| A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques || [https://arxiv.org/pdf/1707.02919.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining Summary] ||<br />
|-<br />
|Week of Nov 30 || Qing Guo, XueGuang Ma, James Ni, Yuanxin Wang || 29|| Mask R-CNN || [https://arxiv.org/pdf/1703.06870.pdf Paper] || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-<br />
|Week of Nov 30 || Daniel Zhang, Jacky Yao, Scholar Sun, Russell Parco, Ian Cheung || 31 || Speech2Face: Learning the Face Behind a Voice || [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform Paper] || ||<br />
|-<br />
|Week of Nov 30 || Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du || 32 || Evaluating Machine Accuracy on ImageNet || [https://proceedings.icml.cc/static/paper_files/icml/2020/6173-Paper.pdf] || ||<br />
|-<br />
|Week of Nov 30 || Msuhi Wang, Siyuan Qiu, Yan Yu || 33 || Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections || [https://ieeexplore.ieee.org/abstract/document/8957421 paper] || ||</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44976Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:17:10Z<p>Jdhchow: </p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_ij}</math> where <math>j</math> in <math>N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_ij = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_ij = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) </math> as <math>\prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
<center>[[Image:FastSBIC.png|800px|]]</center><br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
<center>[[Image:SortedSBIC.png|800px|]]</center><br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
<center>[[Image:Theorem1.png|800px|]]</center><br />
<br />
<center>[[Image:Theorem2.png|800px|]]</center><br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
<center>[[Image:RealWorldResults.png|800px|]]</center><br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
<center>[[Image:TimeRequirement.png|800px|]]</center><br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:TimeRequirement.png&diff=44974File:TimeRequirement.png2020-11-16T21:12:17Z<p>Jdhchow: </p>
<hr />
<div></div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Theorem2.png&diff=44973File:Theorem2.png2020-11-16T21:12:03Z<p>Jdhchow: </p>
<hr />
<div></div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Theorem1.png&diff=44972File:Theorem1.png2020-11-16T21:11:52Z<p>Jdhchow: </p>
<hr />
<div></div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:SortedSBIC.png&diff=44971File:SortedSBIC.png2020-11-16T21:11:40Z<p>Jdhchow: </p>
<hr />
<div></div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:RealWorldResults.png&diff=44970File:RealWorldResults.png2020-11-16T21:11:26Z<p>Jdhchow: </p>
<hr />
<div></div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:FastSBIC.png&diff=44969File:FastSBIC.png2020-11-16T21:11:02Z<p>Jdhchow: </p>
<hr />
<div></div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification&diff=44965Streaming Bayesian Inference for Crowdsourced Classification2020-11-16T21:03:02Z<p>Jdhchow: Created page with "Group 4 Paper Presentation Summary By Jonathan Chow, Nyle Dharani, Ildar Nasirov == Motivation == Crowdsourcing can be a useful tool for data generation in classification pro..."</p>
<hr />
<div>Group 4 Paper Presentation Summary<br />
By Jonathan Chow, Nyle Dharani, Ildar Nasirov<br />
<br />
== Motivation ==<br />
Crowdsourcing can be a useful tool for data generation in classification projects. Often this takes the form of online questions which many respondents will manually answer for payment. One example of this is Amazon’s Mechanical Turk.<br />
<br />
The primary limitation with this form of acquiring data is that respondents are liable to submit incorrect responses.<br />
<br />
The integrity of the data is then limited by how well ground-truth can be determined. The primary method for doing so is probabilistic inference. However, current methods are computationally expensive, lack theoretical guarantees, or are limited to specific settings. <br />
<br />
== Dawid-Skene Model for Crowdsourcing ==<br />
The one-coin Dawid-Skene model is popular for contextualizing crowdsourcing problems. For task <math>i</math> in set <math>M</math>, let the ground-truth be the binary <math>y_i = {\pm 1}</math>. We get labels <math>X = {x_ij}</math> where <math>j</math> in <math>N</math> is the index for that worker.<br />
<br />
At each time step <math>t</math>, a worker <math>j = a(t) </math> provides their label for an assigned task <math>i</math>. We denote responses up to time <math>t</math> via superscript.<br />
<br />
We let <math>x_ij = 0</math> if worker <math>j</math> has not completed task <math>i</math>. We assume that <math>P(x_ij = y_i) = p_j</math>. This implies that each worker is independent and has equal probability of correct labelling regardless of task. In crowdsourcing the data, we must determine how workers are assigned to tasks. We introduce two methods.<br />
<br />
Under uniform sampling, workers are allocated to tasks such that each task is completed by the same number of workers, rounded to the nearest integer, and no worker completes a task more than once. This policy is given by <center><math>\pi_{uni}(t) = argmin_{i \notin M_{a(t)}^t}\{ | N_i^t | \}</math></center><br />
<br />
Under uncertainty sampling, we assign more workers to tasks that are less certain. Assuming, we are able to estimate the posterior probability of ground-truth, we can allocate workers to the task with the lowest probability of falling into the predicted class. This policy is given by <center><math>\pi_{us}(t) = argmin_{i \notin M_{a(t)}^t}\{ (max_{k \in \{\pm 1\}} ( P(y_i = k | X^t) ) \}</math></center><br />
<br />
We then need to aggregate the data. The simple method of majority voting makes predictions for a given task based on the class the most workers have assigned it, <math>\hat{y}_i = sign\{\sum_{j \in N_i} x_{ij}\}</math>.<br />
<br />
== Streaming Bayesian Inference for Crowdsourced Classification ==<br />
The aim of the SBIC algorithm is to estimate the posterior probability, <math>P(y, p | X^t, \theta)</math> where <math>X^t</math> are the observed responses at time <math>t</math> and <math>\theta</math> is our prior. We can then generate predictions <math>\hat{y}^t</math> as the marginal probability over each <math>y_i</math> given <math>X^t</math>, and <math>\theta</math>.<br />
<br />
We factor <math>P(y, p | X^t, \theta) </math> as <math>\prod_{I \in M} \mu_i^t (y_i) \prod_{j \in N} \nu_j^t (p_j) </math> where <math>\mu_i^t</math> corresponds to each task and <math>\nu_j^t</math> to each worker.<br />
<br />
We then sequentially optimize the factors <math>\mu^t</math> and <math>\nu^t</math>. We begin by assuming that the worker accuracy follows a beta distribution with parameters <math>\alpha</math> and <math>\beta</math>. Initialize the task factors <math>\mu_i^0(+1) = q</math> and <math>\mu_i^0(-1) = 1 – q</math> for all <math>i</math>.<br />
<br />
When a new label is observed at time <math>t</math>, we update the <math>\nu_j^t</math> of worker <math>j</math>. We then update <math>\mu_i</math>. These updates are given by<br />
<br />
<center><math>\nu_j^t(p_j) \sim Beta(\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_{ij}) + \alpha, \sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(-x_{ij}) + \beta) </math></center><br />
<br />
<center><math>\mu_i^t(y_i) \propto \begin{cases} \mu_i^{t - 1}(y_i)\overline{p}_j^t & x_{ij} = y_i \\ \mu_i^{t - 1}(y_i)(1 - \overline{p}_j^t) & x_{ij} \ne y_i \end{cases}</math></center><br />
where <math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} \mu_i^{t - 1}(x_ij) + \alpha}{|M_j^{t - 1}| + \alpha + \beta }</math>.<br />
<br />
We choose our predictions to be the maximum <math>\mu_i^t(k) </math> for <math>k=-1,1</math>.<br />
<br />
Depending on our ordering of labels <math>X</math>, we can select for different applications.<br />
<br />
== Fast SBIC ==<br />
The pseudocode for Fast SBIC is shown below.<br />
<br />
As the name implies, the goal with this algorithm is speed. To facilitate this, we leave the order of <math>X</math> unchanged.<br />
<br />
We express <math>\mu_i^t</math> in terms of its log-odds<br />
<center><math>z_i^t = log(\frac{\mu_i^t(+1)}{ \mu_i^t(-1)}) = z_i^{t - 1} + x_{ij} log(\frac{\overline{p}_j^t}{1 - \overline{p}_j^t })</math></center><br />
where <math>x_i^0 = log(\frac{q}{1 - q})</math>.<br />
<br />
The product chain then becomes a summation and removes the need to normalize each <math>\mu_i^t</math>. We use these log-odds to compute worker accuracy.<br />
<br />
<center><math>\overline{p}_j^t = \frac{\sum_{i \in M_j^{t - 1}} sig(x_{ij} z_i^{t-1}) + \alpha}{|M_j^{t - 1}| + \alpha + \beta}</math></center><br />
where <math>sig(z_i^{t-1}) := \frac{1}{1 + exp(-z_i^{t - 1})} = \mu_i^{t - 1}(+1) </math><br />
<br />
The final predictions are made by choosing class <math>\hat{y}_i^T = sign(z_i^T) </math>. We see later that Fast SBIC has similar computational speed to majority voting.<br />
<br />
== Sorted SBIC ==<br />
To increase the accuracy of the SBIC algorithm in exchange for computational efficiency, we run the algorithm in parallel giving labels in different orders. The pseudocode for this algorithm is given below.<br />
<br />
From the general discussion of SBIC, we know that predictions on task <math>i</math> are more accurate toward the end of the collection process. This is a result of observing more data points and having run more updates on <math>\mu_i^t</math> and <math>\nu_j^t</math> to move them further from their prior. This means that task <math>i</math> is predicted more accurately when its corresponding labels are seen closer to the end of the process.<br />
<br />
We take advantage of this property by maintaining a distinct “view” of the log-odds for each task. When a label is observed, we update views for all tasks except the one for which the label was observed. At the end of the collection process, we process skipped labels. When run online, this process must be repeated at every timestep.<br />
<br />
We see that sorted SBIC is slower than Fast SBIC by a factor of M, the number of tasks.<br />
<br />
== Theoretical Analysis ==<br />
The authors prove an exponential relationship between the error probability and number of labels per task. The two theorems, for the different sampling regimes, are presented below.<br />
<br />
== Empirical Analysis ==<br />
The SBIC algorithm is run on five real-world binary classification datasets. The results can be found in the table below. Other algorithms in the comparison are, from left to right, majority voting, expectation-maximization, mean-field, belief-propagation, montecarlo sampling, and triangular estimation. <br />
<br />
In bold are the best performing algorithms for each dataset. We see that both versions of the SBIC algorithm are competitive, having similar prediction error to EM, AMF, and MC. All are considered state-of-the-art Bayesian algorithms.<br />
<br />
The figure below shows the average time required to simulate predictions on synthetic data under an uncertainty sampling policy. We see that Fast SBIC is comparable to majority voting and significantly faster than the other algorithms. This speed improvement, coupled with comparable accuracy, makes the Fast SBIC algorithm powerful.<br />
<br />
== Conclusion and Future Research ==<br />
In conclusion, we have seen that SBIC is computationally efficient, accurate in practice, and has theoretical guarantees. In future, the authors intend to extend the algorithm to the multi-class case.<br />
<br />
== Critique ==<br />
In crowdsourcing data, the cost associated with collecting additional labels is not usually prohibitively expensive. As a result, if there is concern over ground-truth, paying for additional data to ensure <math>X</math> is sufficiently dense may be the desired response as opposed to sacrificing ground-truth accuracy. This may result in the SBIC algorithm being less practically useful than intended.<br />
<br />
== References ==<br />
[1] Manino, Tran-Thanh, and Jennings. Streaming Bayesian Inference for Crowdsourced Classification. 33rd Conference on Neural Information Processing Systems, 2019</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=44956stat441F212020-11-16T19:25:33Z<p>Jdhchow: </p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
[https://www.youtube.com/watch?v=TVLpSFYgF0c&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Influenza_Forecasting_Framework_based_on_Gaussian_Processes Summary]|| [https://www.youtube.com/watch?v=HZG9RAHhpXc&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Gtompkin Summary] || [https://learn.uwaterloo.ca/d2l/ext/rp/577051/lti/framedlaunch/6ec1ebea-5547-46a2-9e4f-e3dc9d79fd54]<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || || [https://www.youtube.com/watch?v=UgVRzi9Ubws]<br />
|-<br />
|Week of Nov 16 || Matthew Hall, Johnathan Chalaturnyk || 5|| Neural Ordinary Differential Equations || [https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_ODEs Summary]||<br />
|-<br />
|Week of Nov 16 || Luwen Chang, Qingyang Yu, Tao Kong, Tianrong Sun || 6|| Adversarial Attacks on Copyright Detection Systems || Paper [https://proceedings.icml.cc/static/paper_files/icml/2020/1894-Paper.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Attacks_on_Copyright_Detection_Systems Summary] ||<br />
|-<br />
|Week of Nov 16 || Casey De Vera, Solaiman Jawad, Jihoon Han || 7|| IPBoost – Non-Convex Boosting via Integer Programming || [https://arxiv.org/pdf/2002.04679.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || Yuxin Wang, Evan Peters, Yifan Mou, Sangeeth Kalaichanthiran || 8|| What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games || [https://arxiv.org/pdf/1805.02777.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=what_game_are_we_playing Summary] || [https://www.youtube.com/watch?v=9qJoVxo3hnI&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Yuchuan Wu || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || Zhou Zeping, Siqi Li, Yuqin Fang, Fu Rao || 10|| A survey of neural network-based cancer prediction models from microarray data || [https://www.sciencedirect.com/science/article/pii/S0933365717305067] || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:J46hou Summary] ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Networks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || Taohao Wang, Zeren Shen, Zihao Guo, Rui Chen || 13|| Deep multiple instance learning for image classification and auto-annotation || [https://jiajunwu.com/papers/dmil_cvpr.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Task_Understanding_from_Confusing_Multi-task_Data Summary] ||<br />
|-<br />
|Week of Nov 23 || Rui Gong, Xuetong Wang, Xinqi Ling, Di Ma || 15|| Semantic Relation Classification via Convolution Neural Network|| [https://www.aclweb.org/anthology/S18-1127.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| Graph Structure of Neural Networks || [https://proceedings.icml.cc/paper/2020/file/757b505cfd34c64c85ca5b5690ee5293-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title= Summary] ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Superhuman AI for multiplayer poker || [https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || Jerry Huang, Daniel Jiang, Minyan Dai || 19|| Neural Speed Reading Via Skim-RNN ||[https://arxiv.org/pdf/1711.02085.pdf?fbclid=IwAR3EeFsKM_b5p9Ox7X9mH-1oI3U3oOKPBy3xUOBN0XvJa7QW2ZeJJ9ypQVo Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_Speed_Reading_via_Skim-RNN Summary]||<br />
|-<br />
|Week of Nov 23 ||Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew || 20|| DivideMix: Learning with Noisy Labels as Semi-supervised Learning || [https://openreview.net/pdf?id=HJgExaVtwr] || ||<br />
|-<br />
|Week of Nov 30 || Banno Dion, Battista Joseph, Kahn Solomon || 21|| Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks || [https://www.sciencedirect.com/science/article/pii/S1877050919310646] || ||<br />
|-<br />
|Week of Nov 30 || Sai Arvind Budaraju, Isaac Ellmen, Dorsa Mohammadrezaei, Emilee Carson || 22|| A universal SNP and small-indel variant caller using deep neural networks||[https://www.nature.com/articles/nbt.4235.epdf?author_access_token=q4ZmzqvvcGBqTuKyKgYrQ9RgN0jAjWel9jnR3ZoTv0NuM3saQzpZk8yexjfPUhdFj4zyaA4Yvq0LWBoCYQ4B9vqPuv8e2HHy4vShDgEs8YxI_hLs9ov6Y1f_4fyS7kGZ Paper] || ||<br />
|-<br />
|Week of Nov 30 || Daniel Fagan, Cooper Brooke, Maya Perelman || 23|| Efficient kNN Classification With Different Number of Nearest Neighbors || [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898482 Paper] || ||<br />
|-<br />
|Week of Nov 30 || Karam Abuaisha, Evan Li, Jason Pu, Nicholas Vadivelu || 24|| Being Bayesian about Categorical Probability || [https://proceedings.icml.cc/static/paper_files/icml/2020/3560-Paper.pdf Paper] || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 30 ||Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee || 26|| Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms || [https://arxiv.org/pdf/1912.07618.pdf?fbclid=IwAR0RwATSn4CiT3qD9LuywYAbJVw8YB3nbex8Kl19OCExIa4jzWaUut3oVB0 Paper] || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&fbclid=IwAR1Tad2DAM7LT6NXXuSYDZtHHBvN0mjZtDdCOiUFFq_XwVcQxG3hU-3XcaE] ||<br />
|-<br />
|Week of Nov 30 || Stan Lee, Seokho Lim, Kyle Jung, Daehyun Kim || 27|| Bag of Tricks for Efficient Text Classification || [https://arxiv.org/pdf/1607.01759.pdf paper] || ||<br />
|-<br />
|Week of Nov 30 || Yawen Wang, Danmeng Cui, ZiJie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid || 28|| A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques || [https://arxiv.org/pdf/1707.02919.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining Summary] ||<br />
|-<br />
|Week of Nov 30 || Qing Guo, XueGuang Ma, James Ni, Yuanxin Wang || 29|| Mask R-CNN || [https://arxiv.org/pdf/1703.06870.pdf Paper] || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-<br />
|Week of Nov 30 || Daniel Zhang, Jacky Yao, Scholar Sun, Russell Parco, Ian Cheung || 31 || Speech2Face: Learning the Face Behind a Voice || [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform Paper] || ||<br />
|-<br />
|Week of Nov 30 || Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du || 32 || Evaluating Machine Accuracy on ImageNet || [https://proceedings.icml.cc/static/paper_files/icml/2020/6173-Paper.pdf] || ||<br />
|-<br />
|Week of Nov 30 || Msuhi Wang, Siyuan Qiu, Yan Yu || 33 || Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections || [https://ieeexplore.ieee.org/abstract/document/8957421 paper] || ||</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=44955stat441F212020-11-16T19:24:51Z<p>Jdhchow: </p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
[https://www.youtube.com/watch?v=TVLpSFYgF0c&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Influenza_Forecasting_Framework_based_on_Gaussian_Processes Summary]|| [https://www.youtube.com/watch?v=HZG9RAHhpXc&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Gtompkin Summary] || [https://learn.uwaterloo.ca/d2l/ext/rp/577051/lti/framedlaunch/6ec1ebea-5547-46a2-9e4f-e3dc9d79fd54] ||<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || [https://www.youtube.com/watch?v=UgVRzi9Ubws] ||<br />
|-<br />
|Week of Nov 16 || Matthew Hall, Johnathan Chalaturnyk || 5|| Neural Ordinary Differential Equations || [https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_ODEs Summary]||<br />
|-<br />
|Week of Nov 16 || Luwen Chang, Qingyang Yu, Tao Kong, Tianrong Sun || 6|| Adversarial Attacks on Copyright Detection Systems || Paper [https://proceedings.icml.cc/static/paper_files/icml/2020/1894-Paper.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Attacks_on_Copyright_Detection_Systems Summary] ||<br />
|-<br />
|Week of Nov 16 || Casey De Vera, Solaiman Jawad, Jihoon Han || 7|| IPBoost – Non-Convex Boosting via Integer Programming || [https://arxiv.org/pdf/2002.04679.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || Yuxin Wang, Evan Peters, Yifan Mou, Sangeeth Kalaichanthiran || 8|| What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games || [https://arxiv.org/pdf/1805.02777.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=what_game_are_we_playing Summary] || [https://www.youtube.com/watch?v=9qJoVxo3hnI&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Yuchuan Wu || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || Zhou Zeping, Siqi Li, Yuqin Fang, Fu Rao || 10|| A survey of neural network-based cancer prediction models from microarray data || [https://www.sciencedirect.com/science/article/pii/S0933365717305067] || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:J46hou Summary] ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Networks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || Taohao Wang, Zeren Shen, Zihao Guo, Rui Chen || 13|| Deep multiple instance learning for image classification and auto-annotation || [https://jiajunwu.com/papers/dmil_cvpr.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Task_Understanding_from_Confusing_Multi-task_Data Summary] ||<br />
|-<br />
|Week of Nov 23 || Rui Gong, Xuetong Wang, Xinqi Ling, Di Ma || 15|| Semantic Relation Classification via Convolution Neural Network|| [https://www.aclweb.org/anthology/S18-1127.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| Graph Structure of Neural Networks || [https://proceedings.icml.cc/paper/2020/file/757b505cfd34c64c85ca5b5690ee5293-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title= Summary] ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Superhuman AI for multiplayer poker || [https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || Jerry Huang, Daniel Jiang, Minyan Dai || 19|| Neural Speed Reading Via Skim-RNN ||[https://arxiv.org/pdf/1711.02085.pdf?fbclid=IwAR3EeFsKM_b5p9Ox7X9mH-1oI3U3oOKPBy3xUOBN0XvJa7QW2ZeJJ9ypQVo Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_Speed_Reading_via_Skim-RNN Summary]||<br />
|-<br />
|Week of Nov 23 ||Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew || 20|| DivideMix: Learning with Noisy Labels as Semi-supervised Learning || [https://openreview.net/pdf?id=HJgExaVtwr] || ||<br />
|-<br />
|Week of Nov 30 || Banno Dion, Battista Joseph, Kahn Solomon || 21|| Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks || [https://www.sciencedirect.com/science/article/pii/S1877050919310646] || ||<br />
|-<br />
|Week of Nov 30 || Sai Arvind Budaraju, Isaac Ellmen, Dorsa Mohammadrezaei, Emilee Carson || 22|| A universal SNP and small-indel variant caller using deep neural networks||[https://www.nature.com/articles/nbt.4235.epdf?author_access_token=q4ZmzqvvcGBqTuKyKgYrQ9RgN0jAjWel9jnR3ZoTv0NuM3saQzpZk8yexjfPUhdFj4zyaA4Yvq0LWBoCYQ4B9vqPuv8e2HHy4vShDgEs8YxI_hLs9ov6Y1f_4fyS7kGZ Paper] || ||<br />
|-<br />
|Week of Nov 30 || Daniel Fagan, Cooper Brooke, Maya Perelman || 23|| Efficient kNN Classification With Different Number of Nearest Neighbors || [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898482 Paper] || ||<br />
|-<br />
|Week of Nov 30 || Karam Abuaisha, Evan Li, Jason Pu, Nicholas Vadivelu || 24|| Being Bayesian about Categorical Probability || [https://proceedings.icml.cc/static/paper_files/icml/2020/3560-Paper.pdf Paper] || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 30 ||Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee || 26|| Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms || [https://arxiv.org/pdf/1912.07618.pdf?fbclid=IwAR0RwATSn4CiT3qD9LuywYAbJVw8YB3nbex8Kl19OCExIa4jzWaUut3oVB0 Paper] || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&fbclid=IwAR1Tad2DAM7LT6NXXuSYDZtHHBvN0mjZtDdCOiUFFq_XwVcQxG3hU-3XcaE] ||<br />
|-<br />
|Week of Nov 30 || Stan Lee, Seokho Lim, Kyle Jung, Daehyun Kim || 27|| Bag of Tricks for Efficient Text Classification || [https://arxiv.org/pdf/1607.01759.pdf paper] || ||<br />
|-<br />
|Week of Nov 30 || Yawen Wang, Danmeng Cui, ZiJie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid || 28|| A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques || [https://arxiv.org/pdf/1707.02919.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining Summary] ||<br />
|-<br />
|Week of Nov 30 || Qing Guo, XueGuang Ma, James Ni, Yuanxin Wang || 29|| Mask R-CNN || [https://arxiv.org/pdf/1703.06870.pdf Paper] || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-<br />
|Week of Nov 30 || Daniel Zhang, Jacky Yao, Scholar Sun, Russell Parco, Ian Cheung || 31 || Speech2Face: Learning the Face Behind a Voice || [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform Paper] || ||<br />
|-<br />
|Week of Nov 30 || Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du || 32 || Evaluating Machine Accuracy on ImageNet || [https://proceedings.icml.cc/static/paper_files/icml/2020/6173-Paper.pdf] || ||<br />
|-<br />
|Week of Nov 30 || Msuhi Wang, Siyuan Qiu, Yan Yu || 33 || Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections || [https://ieeexplore.ieee.org/abstract/document/8957421 paper] || ||</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=42620F21-STAT 441/841 CM 763-Proposal2020-10-07T19:47:28Z<p>Jdhchow: </p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
<br />
--------------------------------------------------------------------<br />
<br />
'''Project # 1 Group members:'''<br />
<br />
Song, Quinn<br />
<br />
Loh, William<br />
<br />
Bai, Junyue<br />
<br />
Choi, Phoebe<br />
<br />
'''Title:''' APTOS 2019 Blindness Detection<br />
<br />
'''Description:'''<br />
<br />
Our team chose the APTOS 2019 Blindness Detection Challenge from Kaggle. The goal of this challenge is to build a machine learning model that detects diabetic retinopathy by screening retina images.<br />
<br />
Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working-aged adults. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). In rural areas where medical screening is difficult to conduct, it is challenging to detect the disease efficiently. Aravind Eye Hospital hopes to utilize machine learning techniques to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.<br />
<br />
Our team plans to solve this problem by applying our knowledge in image processing and classification.<br />
<br />
<br />
----<br />
<br />
'''Project # 2 Group members:'''<br />
<br />
Li, Dylan<br />
<br />
Li, Mingdao<br />
<br />
Lu, Leonie<br />
<br />
Sharman,Bharat<br />
<br />
'''Title:''' Risk prediction in life insurance industry using supervised learning algorithms<br />
<br />
'''Description:'''<br />
<br />
In this project, we aim to replicate and possibly improve upon the work of Jayabalan et al. in their paper “Risk prediction in life insurance industry using supervised learning algorithms”. We will be using the Prudential Life Insurance Data Set that the authors have used and have shared with us. We will be pre-processing the data to replace missing values, using feature selection using CFS and feature reduction using PCA use this processed data to perform Classification via four algorithms – Neural Networks, Random Tree, REPTree and Multiple Linear Regression. We will compare the performance of these Algorithms using MAE and RMSE metrics and come up with visualizations that can explain the results easily even to a non-quantitative audience. <br />
<br />
Our goal behind this project is to learn applying the algorithms that we learned in our class to an industry dataset and come up with results that we can aid better, data-driven decision making.<br />
<br />
----<br />
<br />
'''Project # 3 Group members:'''<br />
<br />
Parco, Russel<br />
<br />
Sun, Scholar<br />
<br />
Yao, Jacky<br />
<br />
Zhang, Daniel<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles<br />
<br />
'''Description:''' <br />
<br />
Our team has decided to participate in the Lyft Motion Prediction for Autonomous Vehicles Kaggle competition. The aim of this competition is to build a model which given a set of objects on the road (pedestrians, other cars, etc), predict the future movement of these objects.<br />
<br />
Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. However, there are still significant engineering challenges to be solved before one can fully realize the benefits of self-driving cars. One such challenge is building models that reliably predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians.<br />
<br />
Our aim is to apply classification techniques learned in class to optimally predict how these objects move.<br />
<br />
----<br />
<br />
'''Project # 4 Group members:'''<br />
<br />
Chow, Jonathan<br />
<br />
Dharani, Nyle<br />
<br />
Nasirov, Ildar<br />
<br />
'''Title:''' Classification with Abstinence<br />
<br />
'''Description:''' <br />
<br />
We seek to implement the algorithm described in [https://papers.nips.cc/paper/9247-deep-gamblers-learning-to-abstain-with-portfolio-theory.pdf Deep Gamblers: Learning to Abstain with Portfolio Theory]. The paper describes augmenting classification problems to include the option of abstaining from making a prediction when confidence is low.<br />
<br />
Medical imaging diagnostics is a field in which classification could assist professionals and improve life expectancy for patients through increased accuracy. However, there are also severe consequences to incorrect predictions. As such, we also hope to apply the algorithm implemented to the classification of medical images, specifically instances of normal and pneumonia [https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia? chest x-rays]. <br />
<br />
----<br />
<br />
'''Project # 5 Group members:'''<br />
<br />
Jones, Hayden<br />
<br />
Leung, Michael<br />
<br />
Haque, Bushra<br />
<br />
Mustatea, Cristian<br />
<br />
'''Title:''' Combine Convolution with Recurrent Networks for Text Classification<br />
<br />
'''Description:''' <br />
<br />
Our team chose to reproduce the paper [https://arxiv.org/pdf/2006.15795.pdf Combine Convolution with Recurrent Networks for Text Classification] on Arxiv. The goal of this paper is to combine CNN and RNN architectures in a way that more flexibly combines the output of both architectures other than simple concatenation through the use of a “neural tensor layer” for the purpose of improving at the task of text classification. In particular, the paper claims that their novel architecture excels at the following types of text classification: sentiment analysis, news categorization, and topical classification. Our team plans to recreate this paper by working in pairs of 2, one pair to implement the CNN pipeline and the other pair to implement the RNN pipeline. We will be working with Tensorflow 2, Google Collab, and reproducing the paper’s experimental results with training on the same 6 publicly available datasets found in the paper.<br />
<br />
----<br />
<br />
'''Project # 6 Group members:'''<br />
<br />
Chin, Ruixian<br />
<br />
Ong, Jason<br />
<br />
Chiew, Wen Cheen<br />
<br />
Tan, Yan Kai<br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction<br />
<br />
'''Description:''' <br />
<br />
Our team chose to participate in a Kaggle research challenge "Mechanisms of Action (MoA) Prediction". This competition is a project within the Broad Institute of MIT and Harvard, the Laboratory for Innovation Science at Harvard (LISH), and the NIH Common Funds Library of Integrated Network-Based Cellular Signatures (LINCS), present this challenge with the goal of advancing drug development through improvements to MoA prediction algorithms.<br />
----<br />
<br />
'''Project # 7 Group members:'''<br />
<br />
Ren, Haotian <br />
<br />
Cheung, Ian Long Yat<br />
<br />
Hussain, Swaleh <br />
<br />
Zahid, Bin, Haris <br />
<br />
'''Title:''' Transaction Fraud Detection <br />
<br />
'''Description:''' <br />
<br />
Protecting people from fraudulent transactions is an important topic for all banks and internet security companies. This Kaggle project is based on the dataset from IEEE Computational Intelligence Society (IEEE-CIS). Our objective is to build a more efficient model in order to recognize each fraud transaction with a higher accuracy and higher speed.<br />
----<br />
<br />
'''Project # 8 Group members:'''<br />
<br />
ZiJie, Jiang<br />
<br />
Yawen, Wang<br />
<br />
DanMeng, Cui<br />
<br />
MingKang, Jiang<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles <br />
<br />
'''Description:'''<br />
<br />
Our team chose to participate in the Kaggle Challenge "Lyft Motion Prediction for Autonomous Vehicles". We will apply our science skills to build motion prediction models for self-driving vehicles. The model will be able to predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians. The goal of this competition is to predict the trajectories of other traffic participants.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=42582F21-STAT 441/841 CM 763-Proposal2020-10-06T20:02:05Z<p>Jdhchow: </p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
<br />
--------------------------------------------------------------------<br />
<br />
'''Project # 1 Group members:'''<br />
<br />
Song, Quinn<br />
<br />
Loh, William<br />
<br />
Bai, Junyue<br />
<br />
Choi, Phoebe<br />
<br />
'''Title:''' APTOS 2019 Blindness Detection<br />
<br />
'''Description:'''<br />
<br />
Our team chose the APTOS 2019 Blindness Detection Challenge from Kaggle. The goal of this challenge is to build a machine learning model that detects diabetic retinopathy by screening retina images.<br />
<br />
Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working-aged adults. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). In rural areas where medical screening is difficult to conduct, it is challenging to detect the disease efficiently. Aravind Eye Hospital hopes to utilize machine learning techniques to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.<br />
<br />
Our team plans to solve this problem by applying our knowledge in image processing and classification.<br />
<br />
<br />
----<br />
<br />
'''Project # 2 Group members:'''<br />
<br />
Li, Dylan<br />
<br />
Li, Mingdao<br />
<br />
Lu, Leonie<br />
<br />
Sharman,Bharat<br />
<br />
'''Title:''' Risk prediction in life insurance industry using supervised learning algorithms<br />
<br />
'''Description:'''<br />
<br />
In this project, we aim to replicate and possibly improve upon the work of Jayabalan et al. in their paper “Risk prediction in life insurance industry using supervised learning algorithms”. We will be using the Prudential Life Insurance Data Set that the authors have used and have shared with us. We will be pre-processing the data to replace missing values, using feature selection using CFS and feature reduction using PCA use this processed data to perform Classification via four algorithms – Neural Networks, Random Tree, REPTree and Multiple Linear Regression. We will compare the performance of these Algorithms using MAE and RMSE metrics and come up with visualizations that can explain the results easily even to a non-quantitative audience. <br />
<br />
Our goal behind this project is to learn applying the algorithms that we learned in our class to an industry dataset and come up with results that we can aid better, data-driven decision making.<br />
<br />
----<br />
<br />
'''Project # 3 Group members:'''<br />
<br />
Parco, Russel<br />
<br />
Sun, Scholar<br />
<br />
Yao, Jacky<br />
<br />
Zhang, Daniel<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles<br />
<br />
'''Description:''' <br />
<br />
Our team has decided to participate in the Lyft Motion Prediction for Autonomous Vehicles Kaggle competition. The aim of this competition is to build a model which given a set of objects on the road (pedestrians, other cars, etc), predict the future movement of these objects.<br />
<br />
Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. However, there are still significant engineering challenges to be solved before one can fully realize the benefits of self-driving cars. One such challenge is building models that reliably predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians.<br />
<br />
Our aim is to apply classification techniques learned in class to optimally predict how these objects move.<br />
<br />
----<br />
<br />
'''Project # 4 Group members:'''<br />
<br />
Chow, Jonathan<br />
<br />
Dharani, Nyle<br />
<br />
Nasirov, Ildar<br />
<br />
'''Title:''' Classification with Abstinence<br />
<br />
'''Description:''' <br />
<br />
We seek to implement the algorithm described in [https://papers.nips.cc/paper/9247-deep-gamblers-learning-to-abstain-with-portfolio-theory.pdf Deep Gamblers: Learning to Abstain with Portfolio Theory]. The paper describes augmenting the classic classification problem to include the option to abstain from making a prediction when uncertainty is high.</div>Jdhchowhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42519stat441F212020-10-06T16:02:37Z<p>Jdhchow: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || || 1|| || || ||<br />
|-<br />
|Week of Nov 16 || || 2|| || || ||<br />
|-<br />
|Week of Nov 16 || || 3|| || || ||<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Netorks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| || || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || || 16|| || || ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Emergent Tool Use From Multi-Agent Autocurricula || [https://arxiv.org/pdf/1909.07528.pdf] || ||<br />
|-<br />
|Week of Nov 23 || || 18|| || || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || || 30|| || || ||<br />
|-</div>Jdhchow