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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]]
||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 || 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]||
|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 || 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]]
|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]]

Revision as of 01:47, 15 November 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] 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
Nov 23 Dylan Spicker 27 Unsupervised Domain Adaptation with Residual Transfer Networks Paper
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
Nov 28 Aditya Sriram 30 Conditional Image Generation with PixelCNN Decoders Paper
Nov 30 Congcong Zhi 31 Dance Dance Convolution Paper Summary
Nov 30 Jian Deng 32 Automated Curriculum Learning for Neural Networks Paper
Nov 30 Elaheh Jalalpour 33