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|Oct 6 || Tameem Adel |Dec 6 || Ali-Akbar Samadani ...

|Nov 6|| Durgesh Saraph |Dec 4 || Project #6 Mohammad Derakhshani || Project #8 Aurelien Quevenne || Project #3 Yao Ya ...

|Mar 1 || Ilia Sucholutsky || 6|| One-Shot Imitation Learning || [https://papers.nips.cc/paper/6709-one-sh |Mar 6 || George (Shiyang) Wen || 7|| AmbientGAN: Generative models from lossy me ...

|Nov 30 ||Project #6 by Ahmed Ibrahim || Project #15 by Jenna Voisin || Project #9 by Ali-Akb ...

|Oct 6 || Joel Smith || || || ...

|Oct 30 || Glen Chalatov || 6 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/pape |Nov 6 || Nargess Heydari || 10 ||Wavelet Pooling For Convolutional Neural Netwo ...

|Oct 6 ||Johnny Chow || Jennifer Smith || Zhe Wang ...

...ed Sorting[http://alex.smola.org/papers/2009/QuaSonSmo09.pdf])|| Yun Wang (6) ...

|Nov 6 || Ali Ghodsi || || Lecturer|||| |Nov 6 || Ali Ghodsi || || Lecturer|||| ...

...aphEliminate on your moral graph, using the elimination ordering (7, 8, 9, 6, 3, 5, 4, 2, 1), and show the resulting reconstituted graph (the graph that (c) Report (b), but using the elimination ordering (7, 6, 8, 9, 4, 3, 2, 5, 1). ...

|Nov 15 ||Project #|| Project #6 by Jeff Glaister || Project #3 Grace Tompkins, Tatianna Krikella, Swaleh H ...

...Hybridization of supervised and unsupervised learning for fraud detection [6] Figure 3: Proposed Architecture [6] ...

== 6. Experiments == ...

...ocols, known as El Escorial criteria, involves a battery of tests taking 3-6 months. This is a considerable amount of time since a quicker diagnosis wou ...owl. Discov. 2015, 29, 1033–1069. [[http://doi.org/10.1007/s10618-014-0386-6 CrossRef]] ...

...enes) while in sparse PCA each involves variables corresponding to at most 6 genes. ...CA (dashed) and the method we explained with <math>k=5</math> and <math>k=6</math> (solid lines). ...

<math>\,\hat{y_i} = \sum_{\alpha}{Q_{i\alpha}l_{\alpha}} </math> (6)<br /> substituting (6) into (7) gives <math>\,K\approx QLQ^T</math> where <math>\,L_{\alpha\beta} ...

...nt labels. This is the simplest and the most inaccurate approach among all 6 methods introduced. The XML-CNN model is compared against the 6 existing competitive methods. The results are as shown in the tables below: ...

6. The re-estimation of <math>\sum_{r_p}</math> is then performed using the s 6. Perform state clustering given the parameters of the untied model in step ...

...Tangxinxin Yao, Jingyue Huang, Ming Fan, Mingguang Liu, Xiaohan Wang || 6|| A New Method of Region Embedding for Text Classification || [https://op ...

For resolution augmentation, 6 scales of input are used, which results in unpooled layer 5 maps of varying (d). The classifier (layers 6,7,8) has a fixed input size of 5x5 and produces a C-dimensional output vect ...

...ch image has 6X6 pixels and each pixel has 8 dimensions. Thus we have 32*6*6 pixels at this point. Consider each pixel is an capsule. We have 32*6*6 capsules <math>u_i</math> from second Conv layer. Thus, we have <math>\hat{ ...

...mes 256</math> tensor from Conv1 and produce an output of a <math>6 \times 6 \times 8</math> tensor. * Size of each convolutional unit: <math>6 \times 6</math>. ...

...of the most common pretext tasks used are rotations and jigsaw puzzle [4,5,6]. As shown in Figure 2, in the rotation task, unlabeled images, <math> </ma \begin{align} \tag{6} \label{eqn:6} ...

...ased NLI systems can be broken by changing words by synonyms or hypernyms [6]. ...antic datasets is a useful means to avoid the problems highlighted in [4,5,6] by means of asking humans to (i) provide counterfactual labels, (ii) retai ...

...the SSL dataset domain has a positive effect, with diminishing ends. Fig. 6(b) shows the effects of shifting the domain of the SSL dataset, by changing <div align="center">Figure 6: (a) Effect of number of images on SSL. (b) Effect of domain shift on SS ...

...lemi, et al. proposed a deep sequence model for premise selection in 2016 [6], and they claim to be the first team to involve deep neural networks in AT ...izar_article.png|thumb|center|Figure 4. An article from MML. Adapted from [6].]] ...

...= \underset{j \ne y_i} \Sigma Q_{ij} \le c, \;\;\; ||XQ||_2 \le 1 \;\;\; (6) </math> In <math>\,(6)</math>, <math>\,Q \in \mathbb{R}^{m\times k}</math> is the dual Lagrange v ...

| Conv 6 || 1 x 1 x 512 || 1 || 56 x 56 x 512 ...rform detection, as shown to be beneficial in Ren et al^{[[#References|[6]]]}. ...

|Nov 20 || Maya(Mahdiyeh) Bayati, Saber Malekmohammadi, Vincent Loung || 6|| Convolutional Neural Networks for Sentence Classification || [https://arxi ...

== 6. Conclusions == ...

...ccurring afterward. Throughout the four blocks, pooling windows are 10, 8, 6, and 4 respectively. Dilated convolutional layers are also used in lieu of ...ghbor up-sampling followed by conventional convolution with kernel sizes 4,6,9 and 10 and batch normalization. The resulting feature maps are then conca ...

...sitive instance in the bag. Some authors combine MIL with Neural Networks[6, 7] and model SMI by max-pooling. This approach is inefficient due to only 6 subtypes of glioma WSI have been tested in this paper: Glioblastoma (GBM), ...

...soon as we enter the building (cross the outermost door) set indoors to 1. 6) As soon as we exit, set indoors to 0. 7) Stop recording. 8) Save data as C ...soon as we enter the building (cross the outermost door) set indoors to 1. 6) Finally, enter a building and ascend/descend to any story. 7) Ascend throu ...

...ight \|_{1})+\lambda_{2}\left \|\mathbf{\theta} \right \|_{2}^{2}, \;\;\;(6) </math></center> ...{\alpha}_{i} \right \|_{1}</math>.<br /><br /> The learning procedure in (6) minimizes the sum of the costs for the pairs <math>(\mathbf{x}_{i},y_{i})_ ...

...eline models, the classic NMT with beam search (NMT-BS)^{[[#References|[6]]]} and the one referred as beam search optimization (NMT-BSO), which ...contains 12M, 4.5M and 10M training data for each task.^{[[#References|[6]]]} ...

...ta is used to generate ground truth labels, such as the Jigsaw puzzle task[6], and the rotation estimation[3]. For example, in the rotation task, we hav * In Jigsaw task [6], the unlabelled images are divided into nine patches and then, the patches ...

== 6. Conclusions == ...

...es are described in the papers by Rabiner and Juang [5] as well as Kalman [6]. The difference with these presentations is that the latent dynamics are c ...einforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26–38. ...

...\leq 1, \; P_1(\textbf{u}) \leq c_1, \; P_2(\textbf{v}) \leq c_2, \;\;\; (6) </math></center> <br /> ...xtbf{v}</math> and the following iterative algorithm can be used to solve (6). <br/> ...

|Oct 31 || ||6 || || || ...

L = L_{CE} + L_{Reg} + \lambda \times L_{MR} \tag{6} \label{eq:op5} <div align="center">'''Figure 6:''' Analyzing Co-Exitation</div> ...

==Project 6: Dimensionality Reduction for Supervised Learning == ...arge-scale noisy anchor-free graph realization. SIAM J. on Sci. Comp., 31, 6, 4351-4372. </ref>. ...

...into three categories: (a) appropriately selecting the neighbors [2] [5] [6] [7] [10]; (b) identifying the outliers [4] [8]; (c) dealing with the insta 6. Optional: extend the analysis to other financial time series, e.g. GDP, un ...

...; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-10 at 6.03.08 PM.png|400px]]</div> ...layers directly to the deep layers are coming from networks like ResNet [6] ...

...le 1. The baseline models are D-MTAE[5],Deep-All (Vanilla AlexNet)[2], DSN[6]and AlexNet+TF[2]. On average, the proposed method outperforms other method ...ctors – pole length and cart mass. In both experiments, we randomly choose 6 source domains for training and hold out 3 domains for (true) testing. Sinc ...

| No conv, 6 full ...3-5% relative improvement over Hybrid DNN. Also CNN-based feature offers 5-6% relative improvement over DNN-based features. ...

...eta}</math> we will get the factorized matrix <math>X\approx QYQ^T</math> (6)<br /> First, starting from the m-dimensional solution of eq. (6), use conjugate gradient methods to maximize the objective function in eq. ...

...ained InceptionV3 network where all layers except the last one are frozen [6] (Footnote). Adding only one poison instance to the training set causes mis [6] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigni ...

...ation of two entities in the same sentence into 6 potential relations. The 6 relations are USAGE, RESULT, MODEL-FEATURE, PART WHOLE, TOPIC, and COMPARE. ...ioned 9 natural language relationships between the word pairs. Among them, 6 potential relationships are USAGE, RESULT, MODEL-FEATURE, PART WHOLE, TOPIC ...

...ionary (DND) used in Neural Episodic Control (NEC) found in [[#References|[6]]], though the gradients from the memory in the MbPA model are not used dur * ^[6]Pritzel. Alexander, Uria. Benigno, Srinivasan. Sriram, Puigdome ...

...le, "AU 1" stands for the inner portion of the brows being raised, and "AU 6" stands for the cheeks being raised. Such a framework helps in describing a ...hich recognizes both permanent and transient AUs with high accuracy rates [6]. Hand-crafted feature descriptors like the LBP are very powerful facial re ...

:<math> X = \{0, 1, 2, 3, 4, 5, 6, 7, 8\} \rightarrow</math>'''State Space''' ...

6. Both natural and random images are found to be vulnerable to adversarial p The result is summarized in Figure 6: ...

...\max_{x_k} \psi(x_j,x_k) \phi(x_k,y_k) \prod_{i!=j} \hat{M}^l_k \,\,\, (6) ...ration uses the messages above as the <math>\hat{M}</math> variables in Eq(6) : ...

...es. Particularly, in the car, person, and rider categories, a 12%, 7%, and 6% higher performance than SharpMask is achieved. File:Figure_3_Neel.JPG|Figure 6: Qualitative results: comparison with human annotator.|alt=alt language ...

...m \; L(y_{ti} , <w_t,x_ti)>) + \gamma \sum_{t=1}^T \; <w_t,D^+w_t> \;\;\; (6)</math></center> ...d in the Reference section) that the function <math>\,R</math> in <math>\,(6)</math> is jointly convex in both <math>\,W</math> and <math>\,D</math>. ...

...rpretations directly in the models, often known as self-explaining models [6, 7, 8, 9]. The alternative option is to generate interpretations in post-ho [6] David Alvarez-Melis and Tommi S Jaakkola. Towards robust interpretability ...

...t reached an accuracy of 72%. Reducing the size of the training dataset to 6 billion caused lower accuracy (66%), which suggests that large amount of th Table 6 shows the empirical comparison between different neural network-based repre ...

...ons are shown to useful for language learning[2]. Several studies[3][4][5][6] have shown that feedback is especially useful in second language learning ...s right”. In the datasets, there are 6 templates for positive feedback and 6 templates for negative feedback, e.g. ”Sorry, that’s not it.”, ”Wrong”, etc ...

...ics simulator to create a simulated environment where robotic spiders with 6 legs are faced with the task of running due east as quickly as possible. Th ...e task of walking east with the torques of two legs scaled by <math> (i-1)/6 </math> ...

== 6 End To End Evaluations == === 6.1 System Implementation === ...

...even learn the emotion of the agents based on their voices. Aytar et al. [6] proposed a student-teacher training procedure in which a well established 6 seconds of audio was used to compute the spectogram by taking a Short-time ...

...ref>R. Smith, "Size of the Moon", Scientific American, 46 (April 1978): 44-6.</ref> ...

...ion-Answering ('''ReQA''') benchmark [5] and used two datasets '''SQuAD'''[6] and '''Natural Questions'''[7] for training and evaluating their models. ...e experiments they did with augmented the dataset as will be seen in table 6. ...

...from each class of the CIFAR-10 validation set. Based on figure 4, 5, and 6, we can see that the <math>L(\delta)</math> (classification loss), <math>T In figure 6 and 7, we can see the effect of <math>\lambda_s</math> on the dissimilarity ...

==Project 6: Application of clustering in bioinformatics: How the structure of a molec ...

...used for training and the others reserved for testing. Table 2 and Figure 6 outlines the result, showing that the proposed RCNN model performs consiste ...EE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052–1067, 2007. ...

...onventional Neural Networks [1,2,3] and their shifted version 3D CNNs [4,5,6] have been employed in action recognition but they identify and aggregate t * 6 Alternative layers with 64, 128, 256, 256, 512 and 512 kernel response maps ...

6. For classification tasks, the idea of learning a “new object” is analogous 6) The simple decaying Hebbian formula in Equation 2 is used to update the He ...

'''Project 6''' ...

\tag{6} \label{6} ...

...x. If interested, the derivation of the whitening equation can be seen in [6]. Li et al. found that whitening removed styles from the image. Authors use $\alpha$ = 0.6 in the style transfer experiments. ...

...ous section (<math>\epsilon = 0.05</math>) gives an approximation of order 6. Additionally, the 8th order and 6th order representations are similar and ...

...ng a fair die repetitively to produce a series of random numbers from 1 to 6). ...917, ''b'' = 11, and ''m'' = 2⁴⁸<ref>http://java.sun.com/javase/6/docs/api/java/util/Random.html#next(int)</ref>. The class returns at most 3 ...

<math>f_{1}=6,8,10,\cdots,20</math> <math>f_{1}=6,8,10,\cdots,20</math> ...

70 & 76 & -20 & -6 \\ ...training and validation sets as the number of epochs increases, and Table 6 shows the accuracy performance. Average pooling demonstrated the highest ac ...

...atasets according to their contextual group <math> X </math> according to [6] and they compare their results using compression ratio <math> \Delta s = \ ...the images mostly taken at night time were selected from DNIM. The figure 6 shows that for DNiM images, the agent's choices are mostly concentrated in ...

...for that task, owing again to the overparameterization of the network [5, 6, 7, 8]. [6] Li, Y. and Liang, Y. (2018). Learning overparameterized neural networks vi ...

...t the exact architecture and experimental setting of the GrammarVAE (GVAE)[6] and replace its variational framework with that of an RAE's utilizing the [6] Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato. Grammar va ...

...ng clean/noisy in order to train the student network on cleaner instances [6]. [[File:Co-Teaching Table 6.png|550px|center]] ...

...cipal component analysis. ''Journal of the Royal Statistical Society, B'', 6(3):611-622, 1999.</ref> ; they found that the closed-form solution for <mat ...ssian Process Latent Variable Models. Journal of Machine Learning Research 6 (2005) 1783–1816. November, 2005. ...

...s the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is furth ...l-attention" has also been implemented in VQA tasks, which is explored in [6]. ...

...sed on the magnitude values so eq. (3) and (4) get transformed to (5) and (6), respectively: ...ilon \quad \{ t_{1}, t_{2},....., t_{k} \} \quad else \quad 0 \quad \quad (6) ...

...arted to appear, for example, the Gaussian Copula Process Volatility model[6]. For this paper, the authors use coupling AR models and neural networks to ...tasets and check how these networks perform. The result is shown in Figure 6. ...

...ities, it will necessarily be higher. The 6.7, for example, indicates that 6.7% of the ground truth tuples appeared in the proposals of the network. ...mages. For this, they used the Visual Genome dataset, with so = 3 and sr = 6. Overall, the new architecture vastly outperformed past models. The results ...

...the TLD framework outperforms previous state-of-arts tracking approaches [6]. 6) Deep learning models like stacked autoencoder have been used to learn good ...

...pics. Therefore, The TCNLM uses a diversity regularizer^{[[#References|[6]]]}^{[[#References|[7]]]} to reduce it. The idea is to regular * ^{[http://www.cs.cmu.edu/~pengtaox/papers/kdd15_drbm.pdf [6]]}P. Xie, Y. Deng, and E. Xing. Diversifying restricted boltzmann mach ...

..._i - y_j \|^2}{\sqrt{D_{ii}D_{jj}}} </math> (6) ...<math>\,\, \mathbf{y=\sigma[-17(\sqrt((\theta_r-\pi)^2+(\theta_p-\pi)^2)-0.6\pi)]} </math> where <math>\,\, \sigma[.] </math> is the [http://en.wikipedi ...

...SQuAD was evaluated using two different models: LSTM+Attention and BiDAF [6]. The first model was inspired by most then-present QA systems consisting o '''Visualization:''' Figure 6 shows that the model does not skim when the input seems to be relevant to a ...

...been widely leveraged for addressing domain generalization [3, 4, 5, 7, 8, 6, 9, 10, 11]. Meta-Learning for domain generalization (MLDG) [4] closely fol ...MetaReg[3], significantly. In addition, the best improvement has achieved (6.20%) when the unseen domain is "sketch", which requires more general knowle ...

<math>W \thicksim U(-L, +L),L = max \left \{ \sqrt{6/n_{in}}, L_{min} \right \}, L_{min} = \beta \sigma</math> MNIST: Network is LeNet-5 variant^{[[#References|[6]]]} with 32C5-MP2-64C5-MP2-512FC-10SSE. ...

...other hand, calculates the similarity using the cosine similarity of BERT [6] contextual embeddings. BertScore basically addresses two common pitfalls i ...with the same hardware, the Machine Translation test on BERTScore takes 15.6 secs compared to 5.4 secs for BLEU. The time range is essentially small and ...

4. The use of dropout, which contributed ~1-6% improvement in the LMH code for different tissues, and ~2-7% in the DNI co ...

...ombining the Probabilistic Label Tree [5] method and the Adaptive Softmax [6] to propose APLC. [6] Grave, E., Joulin, A., Cisse, M., J ´ egou, H., et al. Effi- ´ ...

...</math>, is shown in the top row left column. Here, there are 4 nodes with 6 operations defined between them. ...ormance in those more complex architectures. The first large model (Figure 6) is targeted to image classification on the CIFAR-10 dataset and the second ...

[6] Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, ...

6). Repeat 3 -- 5 until converges ...

...ctions of the Wiener integral, we refer the reader to the textbook by Kuo [6]. Intuitively, the stochastic process <math>X_t</math> can be thought as th ...Gaussian Process Regression, Journal of Machine Learning Research, Volume 6, P1939-1959. ...

...mpting to standardize the field of language understanding tasks. SentEval [6] evaluated fixed-size sentence embeddings for tasks. DecaNLP [7] converts t [6] Alexis Conneau and Douwe Kiela. SentEval: An evaluation toolkit for univer ...

...r multiple hypotheses and long-term interactions between multiple agents" [6]. ...precise global position in urban road environment; 5) Micro-Autobox II and 6) a MDPS are used to control and actuate the subject. All data are stored in ...

As Table 6 above shows, <code>charCNN</code> models performed quite well across differ ..., may 2010. European Language Resources Association (ELRA). ISBN 2-9517408-6-7. URL https://wicopaco.limsi.fr. ...

...er adaptation proposed in this paper and feature adaptation studied in [5, 6] are tailored to adapt different layers of deep networks, they are expected ...data sets, thus providing further adaptation possibilities. This provides 6 Transfer Tasks on the 31 classes of Office-31 ($\{(A,W), (A,D), (W,A), (W,D ...

...s with Lasso (using the regularization path [4]) (scikit lars_path method [6]) and then learning the weights via least squares (together a procedure we [[File:recall1.png|thumb|Figure 6: Recall [11] on truly important features for two interpretable classifiers ...

** 6 categories: bed, bookcase, chair, desk, sofa, table ...2 show the performance of both a single 3D-VAE-GAN jointly trained on all 6 IKEA object categories, and six 3D-VAE-GANs independently trained on each c ...

...ke WaveNet ^{[[#References|[5]]]} and SampleRNN^{[[#References|[6]]]}. These models are effective at modeling short and medium scale (~5 ...h a batch size of 32 with a decaying learning rate ranging from 2e-4 to 6e-6. Here synchronous training refers to the process of training both the encod ...

* Step 6: Global pathway stride-2 deconvolutions, local pathway apply masking operat * Step 6: Orginal keypoint tensor is is concatenated with local and global tensors w ...

...s of the Black-Scholes, Hamilton-Jacobi-Bellman, and Allen-Cahn equations [6], which can be found here: https://arxiv.org/abs/1707.02568. [6] Han, J., Jentzen, A., & Weinan, E. (2018). Solving high-dimensional partia ...

...ies from 6 different neural network controllers that were trained to match 6 different movement styles from the CMU motion capture database. Each trajec ...

...tion of GAN architecture on time-series data like C-RNN-GAN or RCGAN ^[6] try to generate the time-series data recurrently sometimes taking th [6] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. a ...

...completion with the use of depths and silhouettes. A few recent papers [5,6,7,8] discussed enforcing differentiable 2D-3D constraints between shape and Synthesized images of 6,778 chairs from ShapeNet are rendered from 20 random viewpoints. The chairs ...

...content factors (right). Again, in the left and right hand block of Figure 6, each row shows different views generated given the same content. It allows ...

...ng the joint image distribution as a product of conditionals [[#Reference|[6]]]. The Gated PixelCNN is an improvement over the PixelCNN by removing the Now, from Figure 6, notice that as the filter with the mask slides across the image, pixel $f$ ...

6). Repeat the above procedure untill change in EF value is too small (compar ...

[[File:sokoban_noisy.png|800px|center|thumb|Figure 6: Experiments with a noisy environment model Left: each row shows an example ...can compare them to a setup without a rollout decoder. As shown in figure 6(right), even with relatively poor environment model, the performance of I2A ...

6. Yitian Wu TREC question dataset, classifying a question into 6 question types ...

...rent neural network often used in natural language processing. This used a 6-layer architecture. The embedding dimension was varied to vary the complexi [6] Mauro Cettolo, Christian Girardi, and Marcello Federico. Wit3: Web invento ...

...s. The Worker’s recurrent network <math>f^{Wrnn}</math> is a standard LSTM[6]. The baseline the authors are using is a recurrent LSTM[6] network on top of a representation learned by a CNN. The A3C method[5][16] ...

...own in Table 1. The final submission of GoogLeNet obtains a top-5 error of 6.67% on both the validation and testing data, ranking first among all partic ...s a region classifier, combining Selective Search and using an ensemble of 6 CNNs, GoogLeNet gave top detection results, almost doubling accuracy of the ...

...performance of the BP learning algorithm is the presence of local minima [6]. It is undesirable that the learning algorithm stops at a local minima if ...

...G David Garson. Interpreting neural-network connection weights. AI Expert, 6(4):46–51, 1991. [6] Daria Sorokina, Rich Caruana, and Mirek Riedewald. Additive groves of regr ...

...4]), joint embedding space learning ([5]), cross-domain image generation ([6],[7]) to name a few. Thus, the novelty of the authors' contributions to thi [[File:CoGAN-6.PNG]] ...

...ach pixel and spatial smoothing, defends against attacks. Dziugaite et al [6], studied the effect of JPG compression on adversarial images. Chen et al. ...mbination of cropping, TVM, image quilting, and model transfer) by at most 6%. Gains of 1-2% in classification accuracy could be found from ensembling d ...

...own in Table 1. The final submission of GoogLeNet obtains a top-5 error of 6.67% on both the validation and testing data, ranking first among all partic ...s a region classifier, combining Selective Search and using an ensemble of 6 CNNs, GoogLeNet gave top detection results, almost doubling accuracy of the ...

...al k values should be chosen using cross validation while Sahugara et al. [6] proposed using Monte Carlo validation to select varied k parameters. Other [6] F. Sahigara, D. Ballabio, R. Todeschini, and V. Consonni, “Assessing the v ...

...is a constant <math>\,C</math> such that if <math> m \geq C\mu^{2}nr\log^{6}n</math>, then with high probability <math>\,M</math> is the unique solutio ...

==Project 6 : Face Recognition Using Kernel Fisher Linear Discriminant Analysis and RBF ...

[[File:Image of Figure 5.png|thumb|upright=2|center|alt=text|alt=text|Figure 6: Conditional generation of cats (left) and pigs (right).]] ...//devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/ , Apr. 6 2016 ...

...<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-Pai ...includes what method was utilized for data processing(in this case it is a 6-layer CNN), how was the method implemented, etc. There is also another impo ...

...ecifying the end of the input sequence. BERT uses transformer architecture[6] with two training objectives: they use masks language modeling (MLM) and n [6] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, A ...

...stored, where <math>N</math> is some large, finite number (e.g. <math>N=10^6</math>). Incorporating experience replay into the algorithm removes the cor ...dom agent, which is the baseline comparison, chooses a random action every 6 frames (10 Hz). The human player uses the same emulator as the agents, and ...

...ues are available to normalize activations, including batch normalization [6], layer normalization [1] and weight normalization [8]. These methods work ...which layers are not restricted to being sequentially connected [9]; and (6) an FNN-version of residual networks [4], with residual blocks made up of t ...

h^{cat}_{t-1, p} = h_{t-1, p-1} \hspace{1cm} if p > 1 \hspace{1cm} (6) 6. '''<math>C_t \in \mathbb{R}^{P\times M}</math>:''' Memory cell ...

6. The reason behind the empirical success of the method has been argued to b ...

Project # 6 Group members: ...

6. Pan, Lisi ...

...and 5198 test data split into 36 categories. The architecture here starts 6 1D convolutional layers with max-pooling, rather than 3 convolutional layer ...8}-768$. For instance, Ours-$Bnet_{GS}^{C}$ increases the performance of 1.6% compared to $BNet^{C}$. ...

...ion based on the true state during training time [5], expert trajectories [6], human demonstrations [7], or pre-trained object-detection features [8]. I 6. Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea F ...

...ator with $\lambda = 1$. Here ''c'' achieves close to the optimal variance[6] when it is set exactly equal to the state-value function $V_{\pi} (s_i) = ...tity. (This follows immediately by applying the corresponding argument in [6] individually to each term in the sum over ''t'' in Equation 2.) Because th ...

...re mapped to nearby points” (“Vector Representations of Words” 2017, para. 6). Popular RNN structure used in NLP task is long short-term memory (LSTM). [[File:Table6YH.PNG|700px|thumb|centre|Table 6. Sample Word Allocation Table]] ...

6. Xiaoni Lang ...

==Project 6 : Skin Classification== ...

The authors use Adam optimizer with learning rate $1e-6$ and batch size $50$. They first optimize the cross entropy loss for the fi [6] Jason Weston. Dialog-based language learning. In arXiv:1604.06045, 2016. ...

...Keras library was used to augment the data under the conditions in (Table 6). [[File:DBIAPAVUCNN table 6.png]] ...

2. Collect training data in 6 different homes and testing data in 3 homes ...about planar grasping. This was done in parallel using multiple robots in 6 different homes, as shown in Figure 3. They used an object detector (tiny-Y ...

...h size resulted in reducing the number of parameter updates from 14,000 to 6,000. ...appended results do not show validation set accuracy curves like in Figure 6, however. It would be beneficial to see if they were similar to the origina ...

...he GLASSO. The recovered stickmen of EM/RCA and GLASSO are shown in figure 6. ...

...th functions on the high-dimensional sphere. The Annals of Probability, 41(6), 4214-4247.</ref>, an asymptotic evaluation of the complexity of the spher ...

training data. This gain decreases to approximately 6% after interpolation with the back-off language model ...

...d Helmholtz Free Energy. Advances in Neural Information Processing Systems 6 (NIPS 1993). ...

[6] Chapelle, O., Schlkopf, B., and Zien, A. (2010). SemiSupervised Learning. ...

...s only a subset of sampled target words as an align vector to maximize Eq (6), instead of all the likely target words. The most naïve way to select a su | 30.6 ...

[6]: Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, a ...

[[File:6-Turtlebot_visual_2.png | 650px|thumb|center|Figure 5: The performance of Tu [6] Lerrel Pinto and Abhinav Gupta. Supersizing self-supervision: Learning to ...

6 2. See Section 6.1 for details. ...

...he joint probability distribution <math> P(x_{V}) </math>. We have defined 6 tasks (listed bellow) that we would like to accomplish with various algorit which represents a table of probabilities of size <math>2^6</math>. In general this table is of size <math>k^n</math> where <math>k</ma ...

...he number of input elements represented in a state. For instance, stacking 6 blocks with k = 5 results in an input field of 25 elements or we can also s ...4 English to French. The model improves over GNMT in the same setting by 1.6 BLEU on average. It also outperforms their reinforcement (RL) models by 0.5 ...

...unction of the agent during navigation to a destination is shown in Figure 6. [[File:figure6-soroush.png|400px|thumb|center|Figure 6. Trained CityNav agent’s performance in two environments: Central London (l ...

...rks in overcoming exploration difficulties by learning from demonstration [6] and imitation learning in RL. [6] Schaal, S. Learning from demonstration. In Advances in neural information ...

6. Wan Feng Cai ...

...veloped an iOS app called Sensory and used it to collect data on an iPhone 6. The following sensor readings were recorded: indoors, created at, session ...

Project # 6 Group Members: ...

...,...,x_k)=1+\sum_{y=1}^n(\omega_y-1)I(x_1=x_2=...=x_k=y)\text{ }(6)</math></center> ...

Adam optimizer: 6 settings in total, related to ...

</ref>; (6) Breg: low-rank kernel learning with Bregman divergence <ref name="Kulis200 ...

6. Poole, Zach ...

Furthermore, other related reconstruction algorithms can be found in [6] and [7] of the [http://dsp.rice.edu/sites/dsp.rice.edu/files/cs/baraniukCS ...

...such as machine translation. Various types of attention models e.g., soft [6], or location-aware [7], or hard [8, 9] attentions have been proposed in th [6] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine transla ...

..., the dog subset of ImageNet (128*128) was used to train the model. Figure 6 shows that OT-GAN produces less nonsensical images and it has a higher ince ...

...tail in the same [http://www.ai.mit.edu/courses/6.867-f04/lectures/lecture-6-ho.pdf lecture slides]) on the rows of the low-rank matrices. The complexit ...W_k}{I_{ik}}\right]^TV_j\right),\sigma^2)\right]^{I_{ij}},\,\,\,\,\,\,\,\,(6)</math> ...

...on coding various aspects of the original network. The capsule network has 6 overall layers, with the first three layers denoting components of the enco ...vide is: different dimensions are used for the length of the ascender of a 6 and the size of the loop. The variations include stroke thickness, skew and ...

...s a standard CNN. It layers two images on top of one another, resulting in 6 channels (3 RBG channels for each image). It then applies nine convolution ...

[6] Bussell E. H., Dangerfield C. E., Gilligan C. A. and Cunniffe N. J. 2019Ap ...

...rk based generative models; they are autoregressive [4,5] and adversarial [6] based methods. The adversarial model has shown to be very successful in mo ...

Each training sequence is recomposed into 6 subsequences: two forward, two backward, and two identity. To prevent the n ...

| 4:00-6:00 pm If <math>23 = 3 \cdot 6 + 5</math> <br /> ...

...ed15cde28de.pdf 5], [http://ieeexplore.ieee.org/abstract/document/5539893/ 6], [http://openaccess.thecvf.com/content_iccv_2013/papers/Li_Video_Segmentat ...

[6] B. Cestnik et al. Estimating probabilities: a crucial task in machine lear ...

Consider the simple directed graph in Figure 6. [[File:1234.png|thumb|right|Fig.6 Simple 4 node graph.]] ...

[6] Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering ...

...et al. Hadamard matrices and their applications. The Annals of Statistics, 6 (6):1184–1238, 1978. ...

...be more efficient include weight pruning [3,4,5], quantization of weights [6,7] (during or after training), and knowledge distillation [8,9], which trai ...

'''Project # 6''' ...ge target model. Extensive experiments show that our approach leads to a 1.6× and 1.8× speed-up on CIFAR10 and SVHN by selecting 60% and 50% subsets of ...

6. It was mentioned that part of the network used is an "adapted Inception v2 ...

...ltivariate features for <math>\,x</math> and <math>\,y</math> (both having 6 dimensions). Next, they sampled <math>\,z</math> from a bilinear form in <m ...

...F},\mathcal{G})-\text{HSIC}(Z,\mathcal{F},\mathcal{G})|\leq\sqrt{\frac{log(6/\delta)}{\alpha^2m}}+\frac{C}{m}</math><br> ...

<div align="center">Figure 6: Backbone Architecture Experiments</div> ...

6. Constrained EM: EM using side-information in the form of equivalence const ...

...d agent. As we concluded, it provided excellent results as shown in Figure 6. ...

...{x}_{MAP}= argmin_{x'}P(X=x') \textrm{s.t} :y=\phi x'</math> (6)</center> ...

...= 0.8</math> and the standard deviation bonus is set to <math>\gamma_1 = 0.6</math>. ...

'''Project # 6''' ...

6. There is an interesting insight that the idea of the relational graph is s ...

...emantic analysis (1990) [https://doi.org/10.1002/(SICI)1097-4571(199009)41:6&#60;391::AID-ASI1&#62;3.0.CO;2-9], and knowledge discovery in textual datab ...J. Am. Soc. Inf. Sci., 41: 391-407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 ...

6. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchie ...

[6] Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P. Pruning filt ...

Image:neighbours.png|k-nearest neighbours for x, k = 6 ==LLE continued, introduction to maximum variance unfolding (MVU) (Lecture 6: Oct. 1, 2014)== ...

...-ReLU-SO(3)conv-ReLU-FC-softmax and was attempted with bandwidths of 30,10,6 and 20,40,10 channels for each layer respectively. This model was compared ...

...t and cosine distance as the distance function, the classifier achieves 87.6% accuracy on one-shot classification on the ImageNet dataset (Vinyals et al [6] https://hacktilldawn.com/2016/09/25/inception-modules-explained-and-implem ...

...g. The netflix prize. In Proceedings of the KDD Cup Workshop 2007, pages 3–6, New York, Aug. 2007. [6] O. Chapelle and Y. Chang. Yahoo! Learning to Rank Challenge Overview. Jour ...

#Few Sparsity Patterns Shared:6 shared features and 14 site-specific features (out of the 400) are set to b ...

<math>\!P = \begin{pmatrix} 0.2 & 0.8 \\ 0.6 & 0.4 \end{pmatrix}</math> P = [0.2 0.8; 0.6 0.4]; ...

...sional scaling and its recent extension, Isomap, are discussed in Sections 6 and 7 respectively. The last section discusses Semidefinite Embedding, a ne ...

...our data nodes is what is used to process the massive paper data. Hadoop-2.6.5 version in Java is what is used to perform the TF-IDF calculation. Spark [6] Kaufman, L., & Rousseeuw, P. J. (2005). Graphical Output Concerning Each C ...

[6] Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of traini ...

...rchitecture (e.g. by requiring a recurrent model [5] or a Siamese network [6]), and it can be readily combined with fully connected, convolutional, or r ...

parallel branches with rates {6, 12, 18, 24}, which provides the final pixel-wise classification. ...ion cost, inference from the compressed representation costs about <math>0.6 * 10^9</math> FLOPs more. However when accounting for the decoding cost at ...

...s to the monolingual corpora. It is surprising that semi-supervised in row 6 outperforms supervised in row 7, one possible explanation is that both the ...

[6] Guo-Ping Liu, Jian-Jun Yan, Yi-Qin Wang, Jing-Jing Fu, Zhao-Xia Xu, Rui Gu ...

[6] Graves, A. Practical variational inference for neural networks. In Advance ...

...mma=1</math> during evaluation. Also <math display="inline">\beta=0.5, l=0.6</math> in all experiments. 6. VanHasselt,H.,andWiering,M.A. 2009. Usingcontinuousactionspacestosolvedisc ...

...f this paper borrow the concept of experience replay from [[#References|[5,6]]]. In experience replay, we do training in episodes. In each episode, we p [[File:scale_k_p.png|thumb||center||800px|Source: this paper, section 6.1]] ...

[6] C. Villani. Topics in Optimal Transportation. AMS Graduate Studies in Math ...

...ng a fair die repetitively to produce a series of random numbers from 1 to 6). ...917, ''b'' = 11, and ''m'' = 2⁴⁸<ref>http://java.sun.com/javase/6/docs/api/java/util/Random.html#next(int)</ref>. The class returns at most 3 ...

...Switzerland, Switzerland, 2004. Eurographics Association. ISBN 3-905673-12-6. doi: 10.2312/EGWR/EGSR04/023-032. URL http://dx.doi.org/10.2312/EGWR/EGSR0 ...

AlphaGo Zero (Silver et al., 2017, [6]) is an improvement on the AlphaGo Lee algorithm. AlphaGo Zero uses a unifi ...

[http://www.stat.cmu.edu/~larry/=stat707/notes10.pdf See Theorem 46.6 Page 133] [http://www-stat.stanford.edu/~hastie/Papers/RDA-6.pdf] ...

for i=1:6 == Radial Basis Function (RBF) Networks - November 6, 2009 == ...

for i=1:6, semilogy(1:6,error); ...