Difference between revisions of "stat946F18"

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(Record your contributions here [https://docs.google.com/spreadsheets/d/1SxkjNfhOg_eXWpUnVHuIP93E6tEiXEdpm68dQGencgE/edit?usp=sharing])
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|Oct 25 || Dhruv Kumar || 1 || Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs || [https://openreview.net/pdf?id=rkRwGg-0Z Paper] ||  
 
|Oct 25 || Dhruv Kumar || 1 || Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs || [https://openreview.net/pdf?id=rkRwGg-0Z Paper] ||  
 
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Beyond_Word_Importance_Contextual_Decomposition_to_Extract_Interactions_from_LSTMs Summary]
 
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Beyond_Word_Importance_Contextual_Decomposition_to_Extract_Interactions_from_LSTMs Summary]
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[https://wiki.math.uwaterloo.ca/statwiki/images/e/ea/Beyond_Word_Importance.pdf Slides]
 
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|Oct 25 || Amirpasha Ghabussi || 2 || DCN+: Mixed Objective And Deep Residual Coattention for Question Answering || [https://openreview.net/pdf?id=H1meywxRW Paper] ||
 
|Oct 25 || Amirpasha Ghabussi || 2 || DCN+: Mixed Objective And Deep Residual Coattention for Question Answering || [https://openreview.net/pdf?id=H1meywxRW Paper] ||

Revision as of 03:19, 6 November 2018

Project Proposal

Paper presentation

Your feedback on presentations


Record your contributions here [1]

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).




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

Summary Slides

Oct 25 Amirpasha Ghabussi 2 DCN+: Mixed Objective And Deep Residual Coattention for Question Answering Paper

Summary

Oct 25 Juan Carrillo 3 Hierarchical Representations for Efficient Architecture Search Paper

Summary Slides

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

Summary

Nov 1 Sriram Ganapathi Subramanian 7 Differentiable plasticity: training plastic neural networks with backpropagation Paper Summary

Slides

Nov 1 Hadi Nekoei 8 Synthesizing Programs for Images using Reinforced Adversarial Learning Paper Summary

Slides

Nov 1 Henry Chen 9 DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks Paper

Summary Slides

Nov 6 Nargess Heydari 10 Wavelet Pooling For Convolutional Neural Networks Networks Paper Summary
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
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