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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]
|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 26 || Amir-Hossein Karimi || || 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]
|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 26 ||Josh Valchar  || 6|| Learning What and Where to Draw ||[https://papers.nips.cc/paper/6111-learning-what-and-where-to-draw]  ||
|Oct 26 ||Josh Valchar  || 3|| Learning What and Where to Draw ||[https://papers.nips.cc/paper/6111-learning-what-and-where-to-draw]  ||
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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] ||
|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] ||
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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] ||
|Oct 31 || Michael Honke ||5 || 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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|Oct 31 || Almas Rymov || || Analytical Guarantees on Numerical Precision of Deep Neural Networks|| [http://proceedings.mlr.press/v70/sakr17a/sakr17a.pdf Paper]||
|Oct 31 || Almas Rymov ||6 || Analytical Guarantees on Numerical Precision of Deep Neural Networks|| [http://proceedings.mlr.press/v70/sakr17a/sakr17a.pdf Paper]||
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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] ||
|Nov 2 || Prashanth T.K. || 7|| 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 2 || Aditya Sriram || ||Conditional Image Generation with PixelCNN Decoders|| [https://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders.pdf Paper] ||  
|Nov 2 || Aditya Sriram ||8 ||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 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]
|Nov 2 || Haotian Lyu || 9||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 || Dishant Mittal || ||meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting|| [https://arxiv.org/pdf/1706.06197.pdf Paper] ||
|Nov 7 || Dishant Mittal ||10 ||meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting|| [https://arxiv.org/pdf/1706.06197.pdf Paper] ||
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|Nov 7 || Yangjie Zhou|| ||Sequential Neural Models with Stochastic Layers || [https://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf Paper] ||
|Nov 7 || Yangjie Zhou||11 ||Sequential Neural Models with Stochastic Layers || [https://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf Paper] ||
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|Nov 7 ||  Rahul Iyer|| ||Hash Embeddings for Efficient Word Representations ||  NIPS 2017 [https://arxiv.org/pdf/1709.03933.pdf Paper] ||
|Nov 7 ||  Rahul Iyer|| 12||Hash Embeddings for Efficient Word Representations ||  NIPS 2017 [https://arxiv.org/pdf/1709.03933.pdf Paper] ||
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|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] ||
|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] ||
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|Nov 9 || Aravind Balakrishnan || || 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] ||
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|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]  ||
|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]  ||
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|Nov 14 || Avinash Prasad || || 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] ||
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|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]]
|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 || || 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] ||
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|Nov 16 || Hamidreza Shahidi || ||  Teaching Machines to Describe Images via Natural Language Feedback ||  ||
|Nov 16 || Hamidreza Shahidi ||19 ||  Teaching Machines to Describe Images via Natural Language Feedback ||  ||
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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]]
|Nov 16 || Sachin vernekar ||20 || 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 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]
|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 || ||Curiosity-driven Exploration by Self-supervised Prediction  ||[http://proceedings.mlr.press/v70/pathak17a/pathak17a.pdf Paper]||[[Curiosity-driven Exploration by Self-supervised Prediction | Summary]]
||Nov 21 ||Aman Jhunjhunwala ||22 ||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 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]]
|Nov 21 || Peiying Li || 23||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 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]]
|Nov 21 || Ashish Gaurav ||24 || 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 || Venkateshwaran Balasubramanian  || || Large-Scale Evolution of Image Classifiers || [http://proceedings.mlr.press/v70/real17a/real17a.pdf Paper] ||
|Nov 23 || Venkateshwaran Balasubramanian  ||25 || Large-Scale Evolution of Image Classifiers || [http://proceedings.mlr.press/v70/real17a/real17a.pdf Paper] ||
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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]  ||
|Nov 23 || Ershad Banijamali||26 ||Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ||[http://proceedings.mlr.press/v70/finn17a/finn17a.pdf Paper]  ||
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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] ||
|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] ||
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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]]
|Nov 28 || Mike Rudd || 28 || 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 28 || Shivam Kalra || || Still deciding (putting my slot)  || ||
|Nov 28 || Shivam Kalra ||29 || Still deciding (putting my slot)  || ||
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|Nov 28 ||  || || || ||
|Nov 28 ||  ||30 || || ||
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|Nov 30 || Congcong Zhi || || Dance Dance Convolution
|Nov 30 || Congcong Zhi ||31 || 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] ||
|Nov 30 || Jian Deng || 32|| Automated Curriculum Learning for Neural Networks || [http://proceedings.mlr.press/v70/graves17a/graves17a.pdf Paper] ||
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|Nov 30 ||Elaheh Jalalpour  || || ||  ||
|Nov 30 ||Elaheh Jalalpour  || 33|| ||  ||
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Revision as of 15:23, 17 October 2017

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]
Oct 31 Jimit Majmudar 4 Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Paper
Oct 31 Michael Honke 5 A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Paper
Oct 31 Almas Rymov 6 Analytical Guarantees on Numerical Precision of Deep Neural Networks Paper
Nov 2 Prashanth T.K. 7 When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications Paper
Nov 2 Aditya Sriram 8 Conditional Image Generation with PixelCNN Decoders Paper
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
Nov 7 Yangjie Zhou 11 Sequential Neural Models with Stochastic Layers Paper
Nov 7 Rahul Iyer 12 Hash Embeddings for Efficient Word Representations NIPS 2017 Paper
Nov 9 ShuoShuo Liu 13 Learning the Number of Neurons in Deep Networks Paper
Nov 9 Aravind Balakrishnan 14 FeUdal Networks for Hierarchical Reinforcement Learning [2]
Nov 9 Varshanth R Rao 15 Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study Paper
Nov 14 Avinash Prasad 16 Coupled GAN [3]
Nov 14 Nafseer Kadiyaravida 17 Dialog-based Language Learning Paper Summary
Nov 14 Ruifan Yu 18 Imagination-Augmented Agents for Deep Reinforcement Learning Paper
Nov 16 Hamidreza Shahidi 19 Teaching Machines to Describe Images via Natural Language Feedback
Nov 16 Sachin vernekar 20 Natural-Parameter Networks: A Class of Probabilistic Neural Networks Paper Summary
Nov 16 Yunqing He 21 LightRNN: Memory and Computation-Efficient Recurrent Neural Networks [4] Summary
Nov 21 Aman Jhunjhunwala 22 Curiosity-driven Exploration by Self-supervised Prediction Paper Summary
Nov 21 Peiying Li 23 Deep Learning without Poor Local Minima [5] Summary
Nov 21 Ashish Gaurav 24 Deep Exploration via Bootstrapped DQN Paper Summary
Nov 23 Venkateshwaran Balasubramanian 25 Large-Scale Evolution of Image Classifiers Paper
Nov 23 Ershad Banijamali 26 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Paper
Nov 23 Dylan Spicker 27 Unsupervised Domain Adaptation with Residual Transfer Networks Paper
Nov 28 Mike Rudd 28 Deep Transfer Learning with Joint Adaptation Networks Paper Summary
Nov 28 Shivam Kalra 29 Still deciding (putting my slot)
Nov 28 30
Nov 30 Congcong Zhi 31 Dance Dance Convolution
Nov 30 Jian Deng 32 Automated Curriculum Learning for Neural Networks Paper
Nov 30 Elaheh Jalalpour 33