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|Oct 31 || ||6 || || || | |Oct 31 || ||6 || || || | ||
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|Nov 2 || Prashanth T.K. || 7|| When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications||[ | |Nov 2 || Prashanth T.K. || 7|| When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications||[http://proceedings.mlr.press/v70/zhou17c.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_can_Multi-Site_Datasets_be_Pooled_for_Regression%3F_Hypothesis_Tests,_l2-consistency_and_Neuroscience_Applications:_Summary Summary] | ||
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|Nov 2 || |||| || || | |Nov 2 || |||| || || | ||
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|Nov 2 || Haotian Lyu || 9||Learning Important Features Through Propagating Activation Differences|| [http://proceedings.mlr.press/v70/shrikumar17a/shrikumar17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Learning_Important_Features_Through_Propagating_Activation_Differences summary] | |Nov 2 || Haotian Lyu || 9||Learning Important Features Through Propagating Activation Differences|| [http://proceedings.mlr.press/v70/shrikumar17a/shrikumar17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Learning_Important_Features_Through_Propagating_Activation_Differences summary] | ||
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|Nov 7 || Dishant Mittal ||10 ||meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting|| [https://arxiv.org/pdf/1706.06197.pdf Paper] || https://wiki.math.uwaterloo.ca/statwiki/index.php?title= | |Nov 7 || Dishant Mittal ||10 ||meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting|| [https://arxiv.org/pdf/1706.06197.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=meProp:_Sparsified_Back_Propagation_for_Accelerated_Deep_Learning_with_Reduced_Overfitting Summary] | ||
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|Nov 7 || Omid Rezai|| 11 || Understanding the Effective Receptive Field in Deep Convolutional Neural Networks || [https://papers.nips.cc/paper/6203-understanding-the-effective-receptive-field-in-deep-convolutional-neural-networks.pdf Paper]|| | |Nov 7 || Omid Rezai|| 11 || Understanding the Effective Receptive Field in Deep Convolutional Neural Networks || [https://papers.nips.cc/paper/6203-understanding-the-effective-receptive-field-in-deep-convolutional-neural-networks.pdf Paper]|| [[Understanding the Effective Receptive Field in Deep Convolutional Neural Networks | Summary]] | ||
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|Nov 7 || Rahul Iyer|| 12|| | |Nov 7 || Rahul Iyer|| 12|| Convolutional Sequence to Sequence Learning || [https://arxiv.org/pdf/1705.03122.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_Sequence_to_Sequence_Learning Summary] | ||
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|Nov 9 || ShuoShuo Liu ||13 ||Learning the Number of Neurons in Deep Networks|| [http://papers.nips.cc/paper/6372-learning-the-number-of-neurons-in-deep-networks.pdf Paper] || [[Learning the Number of Neurons in Deep Networks | Summary]] | |Nov 9 || ShuoShuo Liu ||13 ||Learning the Number of Neurons in Deep Networks|| [http://papers.nips.cc/paper/6372-learning-the-number-of-neurons-in-deep-networks.pdf Paper] || [[Learning the Number of Neurons in Deep Networks | Summary]] | ||
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|Nov 9 || Aravind Balakrishnan ||14 || FeUdal Networks for Hierarchical Reinforcement Learning || [http://proceedings.mlr.press/v70/vezhnevets17a/vezhnevets17a.pdf] || | |Nov 9 || Aravind Balakrishnan ||14 || FeUdal Networks for Hierarchical Reinforcement Learning || [http://proceedings.mlr.press/v70/vezhnevets17a/vezhnevets17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=FeUdal_Networks_for_Hierarchical_Reinforcement_Learning Summary] | ||
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|Nov 9 || Varshanth R Rao ||15 || Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study || [http://proceedings.mlr.press/v70/ritter17a/ritter17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Cognitive_Psychology_For_Deep_Neural_Networks:_A_Shape_Bias_Case_Study Summary] | |Nov 9 || Varshanth R Rao ||15 || Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study || [http://proceedings.mlr.press/v70/ritter17a/ritter17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Cognitive_Psychology_For_Deep_Neural_Networks:_A_Shape_Bias_Case_Study Summary] | ||
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|Nov 14 || Avinash Prasad ||16 || Coupled GAN|| [https://papers.nips.cc/paper/6544-coupled-generative-adversarial-networks.pdf] || | |Nov 14 || Avinash Prasad ||16 || Coupled GAN|| [https://papers.nips.cc/paper/6544-coupled-generative-adversarial-networks.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Coupled_GAN Summary] | ||
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|Nov 14 || Nafseer Kadiyaravida ||17 || Dialog-based Language Learning || [https://papers.nips.cc/paper/6264-dialog-based-language-learning.pdf Paper] || [[Dialog-based Language Learning | Summary]] | |Nov 14 || Nafseer Kadiyaravida ||17 || Dialog-based Language Learning || [https://papers.nips.cc/paper/6264-dialog-based-language-learning.pdf Paper] || [[Dialog-based Language Learning | Summary]] | ||
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|Nov 14 || Ruifan Yu ||18 || Imagination-Augmented Agents for Deep Reinforcement Learning || [https://arxiv.org/pdf/1707.06203.pdf Paper] || | |Nov 14 || Ruifan Yu ||18 || Imagination-Augmented Agents for Deep Reinforcement Learning || [https://arxiv.org/pdf/1707.06203.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Imagination-Augmented_Agents_for_Deep_Reinforcement_Learning Summary] | ||
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|Nov 16 || Hamidreza Shahidi ||19 || Teaching Machines to Describe Images via Natural Language Feedback || | |Nov 16 || Hamidreza Shahidi ||19 || Teaching Machines to Describe Images via Natural Language Feedback || [https://arxiv.org/pdf/1706.00130 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Teaching_Machines_to_Describe_Images_via_Natural_Language_Feedback Summary] | ||
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|Nov 16 || Sachin vernekar ||20 || | |Nov 16 || Sachin vernekar ||20 || "Why Should I Trust You?": Explaining the Predictions of Any Classifier || [https://arxiv.org/abs/1602.04938 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=%22Why_Should_I_Trust_You%3F%22:_Explaining_the_Predictions_of_Any_Classifier Summary] | ||
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|Nov 16 || Yunqing He ||21 || LightRNN: Memory and Computation-Efficient Recurrent Neural Networks || [https://papers.nips.cc/paper/6512-lightrnn-memory-and-computation-efficient-recurrent-neural-networks] | |Nov 16 || Yunqing He ||21 || LightRNN: Memory and Computation-Efficient Recurrent Neural Networks || [https://papers.nips.cc/paper/6512-lightrnn-memory-and-computation-efficient-recurrent-neural-networks] | ||
|| [[LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | Summary]] | || [[LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | Summary]] | ||
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||Nov 21 ||Aman Jhunjhunwala ||22 || | ||Nov 21 ||Aman Jhunjhunwala ||22 ||Modular Multitask Reinforcement Learning with Policy Sketches ||[http://proceedings.mlr.press/v70/andreas17a/andreas17a.pdf Paper]||[[Modular Multitask Reinforcement Learning with Policy Sketches | Summary]] | ||
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|Nov 21 || Michael Honke ||23 || Universal Style Transfer via Feature Transforms|| [https://arxiv.org/pdf/1705.08086.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Universal_Style_Transfer_via_Feature_Transforms Summary] | |Nov 21 || Michael Honke ||23 || Universal Style Transfer via Feature Transforms|| [https://arxiv.org/pdf/1705.08086.pdf Paper] ||| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Universal_Style_Transfer_via_Feature_Transforms Summary] | ||
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|Nov 21 || | |Nov 21 || Venkateshwaran Balasubramanian ||24 || Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition || [https://papers.nips.cc/paper/6335-deep-alternative-neural-network-exploring-contexts-as-early-as-possible-for-action-recognition.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Alternative_Neural_Network:_Exploring_Contexts_As_Early_As_Possible_For_Action_Recognition Summary] | ||
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|Nov 23 || | |Nov 23 || Ashish Gaurav ||25 || Deep Exploration via Bootstrapped DQN || [https://papers.nips.cc/paper/6501-deep-exploration-via-bootstrapped-dqn.pdf Paper] ||[[Deep Exploration via Bootstrapped DQN | Summary]] | ||
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|Nov 23 || Ershad Banijamali||26 ||Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ||[http://proceedings.mlr.press/v70/finn17a/finn17a.pdf Paper] || | |Nov 23 || Ershad Banijamali||26 ||Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ||[http://proceedings.mlr.press/v70/finn17a/finn17a.pdf Paper] || [[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Summary]] | ||
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|Nov 23 || Dylan Spicker || 27|| Unsupervised Domain Adaptation with Residual Transfer Networks || [https://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks.pdf Paper] || | |Nov 23 || Dylan Spicker || 27|| Unsupervised Domain Adaptation with Residual Transfer Networks || [https://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks.pdf Paper] || [[Unsupervised Domain Adaptation with Residual Transfer Networks | Summary]] | ||
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|Nov 28 || Mike Rudd || 28 || | |Nov 28 || Mike Rudd || 28 || Conditional Image Synthesis with Auxiliary Classifier GANs || [http://proceedings.mlr.press/v70/odena17a.html Paper] || [[Conditional Image Synthesis with Auxiliary Classifier GANs | Summary]] | ||
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|Nov 28 || Shivam Kalra ||29 || | |Nov 28 || Shivam Kalra ||29 || Hierarchical Question-Image Co-Attention for Visual Question Answering || [https://arxiv.org/pdf/1606.00061.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Hierarchical_Question-Image_Co-Attention_for_Visual_Question_Answering Summary] | ||
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|Nov 28 || Aditya Sriram ||30 ||Conditional Image Generation with PixelCNN Decoders|| [https://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders.pdf Paper] || | |Nov 28 || Aditya Sriram ||30 ||Conditional Image Generation with PixelCNN Decoders|| [https://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Conditional_Image_Generation_with_PixelCNN_Decoders Summary] | ||
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|Nov 30 || Congcong Zhi ||31 || | |Nov 30 || Congcong Zhi ||31 || Decoding with Value Networks for Neural Machine Translation | ||
|| [ | || [https://nips.cc/Conferences/2017/Schedule?showEvent=8815 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Decoding_with_Value_Networks_for_Neural_Machine_Translation Summary] | ||
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|Nov 30 || Jian Deng || 32|| Automated Curriculum Learning for Neural Networks || [http://proceedings.mlr.press/v70/graves17a/graves17a.pdf Paper] || | |Nov 30 || Jian Deng || 32|| Automated Curriculum Learning for Neural Networks || [http://proceedings.mlr.press/v70/graves17a/graves17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Automated_Curriculum_Learning_for_Neural_Networks Summary] | ||
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|Nov 30 ||Elaheh Jalalpour || 33|| || || | |Nov 30 ||Elaheh Jalalpour || 33|| || || |
Latest revision as of 19:28, 19 November 2020
List of Papers
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Paper presentation
Date | Name | Paper number | Title | Link to the paper | Link to the summary |
Oct 12 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | Summary | |
Oct 26 | Sakif Khan | 1 | Improved Variational Inference with Inverse Autoregressive Flow | Paper | Summary |
Oct 26 | Amir-Hossein Karimi | 2 | Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling | Paper | Summary |
Oct 26 | Josh Valchar | 3 | Learning What and Where to Draw | [1] | Summary |
Oct 31 | Jimit Majmudar | 4 | Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition | Paper | Summary |
Oct 31 | 6 | ||||
Nov 2 | Prashanth T.K. | 7 | When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications | Paper | Summary |
Nov 2 | |||||
Nov 2 | Haotian Lyu | 9 | Learning Important Features Through Propagating Activation Differences | Paper | summary |
Nov 7 | Dishant Mittal | 10 | meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting | Paper | Summary |
Nov 7 | Omid Rezai | 11 | Understanding the Effective Receptive Field in Deep Convolutional Neural Networks | Paper | Summary |
Nov 7 | Rahul Iyer | 12 | Convolutional Sequence to Sequence Learning | Paper | Summary |
Nov 9 | ShuoShuo Liu | 13 | Learning the Number of Neurons in Deep Networks | Paper | Summary |
Nov 9 | Aravind Balakrishnan | 14 | FeUdal Networks for Hierarchical Reinforcement Learning | Paper | Summary |
Nov 9 | Varshanth R Rao | 15 | Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study | Paper | Summary |
Nov 14 | Avinash Prasad | 16 | Coupled GAN | [2] | Summary |
Nov 14 | Nafseer Kadiyaravida | 17 | Dialog-based Language Learning | Paper | Summary |
Nov 14 | Ruifan Yu | 18 | Imagination-Augmented Agents for Deep Reinforcement Learning | Paper | Summary |
Nov 16 | Hamidreza Shahidi | 19 | Teaching Machines to Describe Images via Natural Language Feedback | Paper | Summary |
Nov 16 | Sachin vernekar | 20 | "Why Should I Trust You?": Explaining the Predictions of Any Classifier | Paper | Summary |
Nov 16 | Yunqing He | 21 | LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | [3] | Summary |
Nov 21 | Aman Jhunjhunwala | 22 | Modular Multitask Reinforcement Learning with Policy Sketches | Paper | Summary |
Nov 21 | Michael Honke | 23 | Universal Style Transfer via Feature Transforms | Paper | Summary |
Nov 21 | Venkateshwaran Balasubramanian | 24 | Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition | Paper | Summary |
Nov 23 | Ashish Gaurav | 25 | Deep Exploration via Bootstrapped DQN | Paper | Summary |
Nov 23 | Ershad Banijamali | 26 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Paper | Summary |
Nov 23 | Dylan Spicker | 27 | Unsupervised Domain Adaptation with Residual Transfer Networks | Paper | Summary |
Nov 28 | Mike Rudd | 28 | Conditional Image Synthesis with Auxiliary Classifier GANs | Paper | Summary |
Nov 28 | Shivam Kalra | 29 | Hierarchical Question-Image Co-Attention for Visual Question Answering | Paper | Summary |
Nov 28 | Aditya Sriram | 30 | Conditional Image Generation with PixelCNN Decoders | Paper | Summary |
Nov 30 | Congcong Zhi | 31 | Decoding with Value Networks for Neural Machine Translation | Paper | Summary |
Nov 30 | Jian Deng | 32 | Automated Curriculum Learning for Neural Networks | Paper | Summary |
Nov 30 | Elaheh Jalalpour | 33 |