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E: You had an editorial contribution on a  paper (excluding the paper that you present).
 
E: You had an editorial contribution on a  paper (excluding the paper that you present).
  
[https://goo.gl/forms/9lt6ZOQdCmwsPgfn2J Your feedback on presentations]
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[https://docs.google.com/forms/d/e/1FAIpQLSf9DIuUylcR-HCN_ts-uP-10jE4wDuMuzTA4vg3r2KR_uHRWQ/viewform?vc=0&c=0&w=1J Your feedback on presentations]
  
 
=Paper presentation=
 
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|Oct 12 (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] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]
 
|Oct 12 (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] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]
 
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|Oct 24 || Sakif Khan  || 1|| Improved Variational Inference with Inverse Autoregressive Flow || [https://papers.nips.cc/paper/6581-improved-variational-inference-with-inverse-autoregressive-flow]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Improved_Variational_Inference_with_Inverse_Autoregressive_Flow]
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|Oct 26 || Sakif Khan  || 1|| Improved Variational Inference with Inverse Autoregressive Flow || [https://papers.nips.cc/paper/6581-improved-variational-inference-with-inverse-autoregressive-flow Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Improved_Variational_Inference_with_Inverse_Autoregressive_Flow Summary]
 
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|Oct 24 || Amir-Hossein Karimi || || Neural Architecture Search With Reinforcement Learning || [https://2017.icml.cc/Conferences/2017/Schedule?showEvent=634] ||
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|Oct 26 || Amir-Hossein Karimi ||2 || Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling || [https://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Learning_a_Probabilistic_Latent_Space_of_Object_Shapes_via_3D_GAN Summary]
 
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|Oct 24 ||  ||3 ||  ||||
 
 
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|Oct 26 ||  ||4 || ||||
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|Oct 26 ||Josh Valchar || 3|| Learning What and Where to Draw ||[https://papers.nips.cc/paper/6111-learning-what-and-where-to-draw]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_What_and_Where_to_Draw Summary]
 
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|Oct 26 ||  || 5|| ||||
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|Oct 31 ||Jimit Majmudar ||4 ||Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition || [https://papers.nips.cc/paper/6258-incremental-boosting-convolutional-neural-network-for-facial-action-unit-recognition.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Incremental_Boosting_Convolutional_Neural_Network_for_Facial_Action_Unit_Recognition Summary]
 
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|Oct 26 ||  || 6|| ||  ||
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|Oct 31 ||  ||6 || ||  ||
 
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|Oct 31 ||Jimit Majmudar  || ||Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition || [https://papers.nips.cc/paper/6258-incremental-boosting-convolutional-neural-network-for-facial-action-unit-recognition.pdf Paper] ||
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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||[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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|Oct 31 || Michael Honke || || A Theoretically Grounded Application of Dropout in Recurrent Neural Networks|| [https://papers.nips.cc/paper/6241-a-theoretically-grounded-application-of-dropout-in-recurrent-neural-networks.pdf Paper] ||
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|Nov 2 || |||| || ||
 
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|Oct 31 || Almas Rymov || || Analytical Guarantees on Numerical Precision of Deep Neural Networks|| http://proceedings.mlr.press/v70/sakr17a/sakr17a.pdf ||
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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]
 
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|Nov 2 || Prashanth T.K. || || When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications||[https://proceedings.mlr.press/v70/zhou17c/zhou17c.pdf Paper] ||
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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=meProp:_Sparsified_Back_Propagation_for_Accelerated_Deep_Learning_with_Reduced_Overfitting Summary]
 
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|Nov 2 || Aditya Sriram || ||Conditional Image Generation with PixelCNN Decoders|| [https://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders.pdf Paper] ||  
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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]|| [[Understanding the Effective Receptive Field in Deep Convolutional Neural Networks | Summary]]
 
|-
 
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|Nov 2 || Haotian Lyu || ||Learning Important Features Through Propagating Activation Differences|| [http://proceedings.mlr.press/v70/shrikumar17a/shrikumar17a.pdf Paper] || [http://www.shortscience.org/paper?bibtexKey=conf/icml/ShrikumarGK17 summary]
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|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 7 || Dishant Mittal || ||meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting|| [https://arxiv.org/pdf/1706.06197.pdf Paper] ||
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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]]
 
|-
 
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|Nov 7 || Yangjie Zhou|| ||An Alternative Softmax Operator for Reinforcement Learning || [http://proceedings.mlr.press/v70/asadi17a/asadi17a.pdf Paper] ||
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|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 7 || Rahul Iyer|| ||Hash Embeddings for Efficient Word Representations || NIPS 2017 [https://arxiv.org/pdf/1709.03933.pdf Paper] ||
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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 || ShuoShuo Liu || ||Learning the Number of Neurons in Deep Networks|| [http://papers.nips.cc/paper/6372-learning-the-number-of-neurons-in-deep-networks.pdf Paper] ||
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|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 9 || Aravind Balakrishnan || || FeUdal Networks for Hierarchical Reinforcement Learning || [http://proceedings.mlr.press/v70/vezhnevets17a/vezhnevets17a.pdf] ||
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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 9 || Varshanth R Rao || || Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study || [http://proceedings.mlr.press/v70/ritter17a/ritter17a.pdf Paper] ||
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|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 14 || Avinash Prasad || || Coupled GAN|| [https://papers.nips.cc/paper/6544-coupled-generative-adversarial-networks.pdf] ||
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|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]
 
|-
 
|-
|Nov 14 || Nafseer Kadiyaravida || || 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 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]
 
|-
 
|-
|Nov 14 || Ruifan Yu || || Imagination-Augmented Agents for Deep Reinforcement Learning || [https://arxiv.org/pdf/1707.06203.pdf Paper] ||
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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]
 +
|| [[LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | Summary]]
 
|-
 
|-
|Nov 16 || Hamidreza Shahidi || ||  Teaching Machines to Describe Images via Natural Language Feedback || ||
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||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 16 || Sachin vernekar || || Natural-Parameter Networks: A Class of Probabilistic Neural Networks  || [https://papers.nips.cc/paper/6279-natural-parameter-networks-a-class-of-probabilistic-neural-networks Paper ] || [[Natural-Parameter Networks: A Class of Probabilistic Neural Networks | 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]
 
|-
 
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|Nov 16 || Yunqing HE || || LightRNN: Memory and Computation-Efficient Recurrent Neural Networks  || [https://papers.nips.cc/paper/6512-lightrnn-memory-and-computation-efficient-recurrent-neural-networks]
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|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 21 ||Aman Jhunjhunwala || ||Curiosity-driven Exploration by Self-supervised Prediction  ||[http://proceedings.mlr.press/v70/pathak17a/pathak17a.pdf Paper]||[[Curiosity-driven Exploration by Self-supervised Prediction | Summary]]
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|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 21 || Peiying Li || ||Deep Learning without Poor Local Minima || [https://papers.nips.cc/paper/6112-deep-learning-without-poor-local-minima.pdf] || [[Deep Learning without Poor Local Minima | 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] || [[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Summary]]
 
|-
 
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|Nov 21 || Ashish Gaurav || || 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 || 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 23 || Venkateshwaran Balasubramanian  || || Large-Scale Evolution of Image Classifiers || [http://proceedings.mlr.press/v70/real17a/real17a.pdf Paper] ||
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|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 23 || Ershad Banijamali|| ||Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ||[http://proceedings.mlr.press/v70/finn17a/finn17a.pdf Paper] ||
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|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 23 || Dylan Spicker || || Unsupervised Domain Adaptation with Residual Transfer Networks || [https://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks.pdf Paper] ||
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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] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Conditional_Image_Generation_with_PixelCNN_Decoders Summary]
 
|-
 
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|Nov 28 || Mike Rudd || 1 || Deep Transfer Learning with Joint Adaptation Networks || [https://2017.icml.cc/Conferences/2017/Schedule?showEvent=470 Paper] || [[Deep Transfer Learning with Joint Adaptation Networks | Summary]]
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|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 28 || Shivam Kalra || || Still deciding (putting my slot)  || ||
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|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 28 || Ningsheng Zhao || || Robust Probabilistic Modeling with Bayesian Data Reweighting || [http://proceedings.mlr.press/v70/wang17g/wang17g.pdf ] ||
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|Nov 30 ||Elaheh Jalalpour || 33|| ||  ||
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|Nov 30 || Congcong Zhi || || Dance Dance Convolution
 
||  ||
 
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|Nov 30 || Jian Deng || || Automated Curriculum Learning for Neural Networks || [http://proceedings.mlr.press/v70/graves17a/graves17a.pdf Paper] ||
 
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|Nov 30 ||  || || ||  ||
 
 
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Latest revision as of 18:28, 19 November 2020

List of Papers

Record your contributions here:

Use the following notations:

P: You have written a summary/critique on the paper.

T: You had a technical contribution on a paper (excluding the paper that you present).

E: You had an editorial contribution on a paper (excluding the paper that you present).

Your feedback on presentations

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