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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]] | |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 || Hierarchical Question-Image Co-Attention for Visual Question Answering || [https://arxiv.org/pdf/1606.00061.pdf Paper] || | |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] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Conditional_Image_Generation_with_PixelCNN_Decoders Summary] | |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] |
Revision as of 07:25, 19 November 2017
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 | Dance Dance Convolution | Paper | Summary |
Nov 30 | Jian Deng | 32 | Automated Curriculum Learning for Neural Networks | Paper | |
Nov 30 | Elaheh Jalalpour | 33 |