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|Nov 6 || Nargess Heydari || 10 ||Wavelet Pooling For Convolutional Neural Networks Networks || [https://openreview.net/pdf?id=rkhlb8lCZ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Wavelet_Pooling_For_Convolutional_Neural_Networks Summary] [https://wiki.math.uwaterloo.ca/statwiki/images/1/1a/Wavelet_Pooling_for_Convolutional_Neural_Networks.pptx Slides] | |Nov 6 || Nargess Heydari || 10 ||Wavelet Pooling For Convolutional Neural Networks Networks || [https://openreview.net/pdf?id=rkhlb8lCZ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Wavelet_Pooling_For_Convolutional_Neural_Networks Summary] [https://wiki.math.uwaterloo.ca/statwiki/images/1/1a/Wavelet_Pooling_for_Convolutional_Neural_Networks.pptx Slides] | ||
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|Nov 6 || Aravind Ravi || 11 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Towards_Image_Understanding_From_Deep_Compression_Without_Decoding Summary] | |Nov 6 || Aravind Ravi || 11 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946w18/Towards_Image_Understanding_From_Deep_Compression_Without_Decoding Summary] || | ||
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:DL_STAT946_PPT_AravindRavi.pdf Slides] | |||
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|Nov 6 || Ronald Feng || 12 || Learning to Teach || [https://openreview.net/pdf?id=HJewuJWCZ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Teach Summary] | |Nov 6 || Ronald Feng || 12 || Learning to Teach || [https://openreview.net/pdf?id=HJewuJWCZ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Teach Summary] |
Revision as of 23:32, 6 November 2018
Project Proposal
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Record your contributions here [1]
Use the following notations:
P: You have written a summary/critique on the paper.
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E: You had an editorial contribution on a paper (excluding the paper that you present).
Date | Name | Paper number | Title | Link to the paper | Link to the summary | |
Feb 15 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | [Summary] | ||
Oct 25 | Dhruv Kumar | 1 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | Paper | ||
Oct 25 | Amirpasha Ghabussi | 2 | DCN+: Mixed Objective And Deep Residual Coattention for Question Answering | Paper | ||
Oct 25 | Juan Carrillo | 3 | Hierarchical Representations for Efficient Architecture Search | Paper | ||
Oct 30 | Manpreet Singh Minhas | 4 | End-to-end Active Object Tracking via Reinforcement Learning | Paper | Summary | |
Oct 30 | Marvin Pafla | 5 | Fairness Without Demographics in Repeated Loss Minimization | Paper | Summary | |
Oct 30 | Glen Chalatov | 6 | Pixels to Graphs by Associative Embedding | Paper | ||
Nov 1 | Sriram Ganapathi Subramanian | 7 | Differentiable plasticity: training plastic neural networks with backpropagation | Paper | Summary | |
Nov 1 | Hadi Nekoei | 8 | Synthesizing Programs for Images using Reinforced Adversarial Learning | Paper | Summary | |
Nov 1 | Henry Chen | 9 | DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks | Paper | ||
Nov 6 | Nargess Heydari | 10 | Wavelet Pooling For Convolutional Neural Networks Networks | Paper | Summary Slides | |
Nov 6 | Aravind Ravi | 11 | Towards Image Understanding from Deep Compression Without Decoding | Paper | Summary | |
Nov 6 | Ronald Feng | 12 | Learning to Teach | Paper | Summary | |
Nov 8 | Neel Bhatt | 13 | Annotating Object Instances with a Polygon-RNN | Paper | Summary | |
Nov 8 | Jacob Manuel | 14 | Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels | Paper | Summary | |
Nov 8 | Charupriya Sharma | 15 | Tighter Variational Bounds are Not Necessarily Better | Paper | Summary | |
NOv 13 | Sagar Rajendran | 16 | Zero-Shot Visual Imitation | Paper | Summary | |
Nov 13 | Ruijie Zhang | 17 | Searching for Efficient Multi-Scale Architectures for Dense Image Prediction | Paper | ||
Nov 13 | Neil Budnarain | 18 | Predicting Floor Level For 911 Calls with Neural Network and Smartphone Sensor Data | Paper | Summary | |
NOv 15 | Zheng Ma | 19 | Reinforcement Learning of Theorem Proving | Paper | ||
Nov 15 | Abdul Khader Naik | 20 | ||||
Nov 15 | Johra Muhammad Moosa | 21 | Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin | Paper | ||
NOv 20 | Zahra Rezapour Siahgourabi | 22 | ||||
Nov 20 | Shubham Koundinya | 23 | TBD | |||
Nov 20 | Salman Khan | 24 | Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | paper | ||
NOv 22 | Soroush Ameli | 25 | Learning to Navigate in Cities Without a Map | paper | ||
Nov 22 | Ivan Li | 26 | Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction | Paper | ||
Nov 22 | Sigeng Chen | 27 | ||||
Nov 27 | Aileen Li | 28 | Spatially Transformed Adversarial Examples | Paper | ||
NOv 27 | Xudong Peng | 29 | Multi-Scale Dense Networks for Resource Efficient Image Classification | Paper | ||
Nov 27 | Xinyue Zhang | 30 | An Inference-Based Policy Gradient Method for Learning Options | Paper | ||
NOv 29 | Junyi Zhang | 31 | Autoregressive Convolutional Neural Networks for Asynchronous Time Series | Paper | ||
Nov 29 | Travis Bender | 32 | Automatic Goal Generation for Reinforcement Learning Agents | Paper | ||
Nov 29 | Patrick Li | 33 | Matrix Capsules with EM Routing | Paper | ||
Makeup | Jiazhen Chen | 34 | ||||
Makeup | Ahmed Afify | 35 | Don't Decay the Learning Rate, Increase the Batch Size | Paper | ||
Makeup | Gaurav Sahu | 36 | TBD | |||
Makeup | Kashif Khan | 37 | Wasserstein Auto-Encoders | Paper | ||
Makeup | Shala Chen | 38 | A NEURAL REPRESENTATION OF SKETCH DRAWINGS | |||
Makeup | Ki Beom Lee | 39 | Detecting Statistical Interactions from Neural Network Weights | Paper | ||
Makeup | Wesley Fisher | 40 | Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling | Paper | Summary |