Difference between revisions of "stat946F18"

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|Nov 13 || Neil Budnarain  || 18 || Predicting Floor Level For 911 Calls with Neural Networks and Smartphone Sensor Data || [https://openreview.net/pdf?id=ryBnUWb0b Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Floor_Level_For_911_Calls_with_Neural_Network_and_Smartphone_Sensor_Data  Summary]
 
|Nov 13 || Neil Budnarain  || 18 || Predicting Floor Level For 911 Calls with Neural Networks and Smartphone Sensor Data || [https://openreview.net/pdf?id=ryBnUWb0b Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Floor_Level_For_911_Calls_with_Neural_Network_and_Smartphone_Sensor_Data  Summary]
 
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|NOv 15 ||  Zheng Ma || 19 || Reinforcement Learning of Theorem Proving  ||  [https://arxiv.org/abs/1805.07563 Paper] ||  
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|NOv 15 ||  Zheng Ma || 19 || Reinforcement Learning of Theorem Proving  ||  [https://arxiv.org/abs/1805.07563 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Reinforcement_Learning_of_Theorem_Proving Summary] [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:zheng_946_presentation.pdf Slides]
 
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|Nov 15 || Abdul Khader Naik  || 20 || Multi-View Data Generation Without View Supervision || [https://openreview.net/pdf?id=ryRh0bb0Z Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=MULTI-VIEW_DATA_GENERATION_WITHOUT_VIEW_SUPERVISION Summary]
 
|Nov 15 || Abdul Khader Naik  || 20 || Multi-View Data Generation Without View Supervision || [https://openreview.net/pdf?id=ryRh0bb0Z Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=MULTI-VIEW_DATA_GENERATION_WITHOUT_VIEW_SUPERVISION Summary]
 
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|Nov 15 || Johra Muhammad Moosa  || 21 || Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Attend_and_Predict:_Understanding_Gene_Regulation_by_Selective_Attention_on_Chromatin Summary]
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|Nov 15 || Johra Muhammad Moosa  || 21 || Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Attend_and_Predict:_Understanding_Gene_Regulation_by_Selective_Attention_on_Chromatin Summary] [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Attend_and_Predict.pdf Slides]
 
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|NOv 20 || Zahra Rezapour Siahgourabi  || 22 ||Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias  ||[https://arxiv.org/pdf/1807.07049 Paper]  ||  
 
|NOv 20 || Zahra Rezapour Siahgourabi  || 22 ||Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias  ||[https://arxiv.org/pdf/1807.07049 Paper]  ||  
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[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Robot_Learning_in_Homes:_Improving_Generalization_and_Reducing_Dataset_Bias Summary]
 
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|Nov 20 || Shubham Koundinya  || 23 || Countering Adversarial Images Using Input Transformations ||  ||  
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|Nov 20 || Shubham Koundinya  || 23 || Countering Adversarial Images Using Input Transformations ||[https://openreview.net/pdf?id=SyJ7ClWCb paper]   ||  
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[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Countering_Adversarial_Images_Using_Input_Transformations Summary]
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[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Countering_Adversarial_Images.pdf Slides]
 
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|Nov 20 || Salman Khan  || 24 || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper]  ||  
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|Nov 20 || Salman Khan  || 24 || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Obfuscated_Gradients_Give_a_False_Sense_of_Security_Circumventing_Defenses_to_Adversarial_Examples Summary]
 
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|NOv 22 ||Soroush Ameli  || 25 || Learning to Navigate in Cities Without a Map ||  [https://arxiv.org/abs/1804.00168 paper] ||  
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|NOv 22 ||Soroush Ameli  || 25 || Learning to Navigate in Cities Without a Map ||  [https://arxiv.org/abs/1804.00168 paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_to_Navigate_in_Cities_Without_a_Map Summary]
 
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|Nov 22 ||Ivan Li || 26 || Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction || [https://arxiv.org/pdf/1802.05451v3.pdf Paper] ||
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|Nov 22 ||Ivan Li || 26 || Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction || [https://arxiv.org/pdf/1802.05451v3.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Mapping_Images_to_Scene_Graphs_with_Permutation-Invariant_Structured_Prediction Summary]
 
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|Nov 22 ||Sigeng Chen || 27 ||GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models || [http://proceedings.mlr.press/v80/you18a.html Paper] ||
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|Nov 22 ||Sigeng Chen || 27 ||Conditional Neural Processes || [http://proceedings.mlr.press/v80/garnelo18a/garnelo18a.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=conditional_neural_process Summary]
 
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|Nov 27 || Aileen Li  || 28 || Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper]   ||  
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|Nov 27 || Aileen Li  || 28 || Unsupervised Neural Machine Translation ||[https://openreview.net/pdf?id=Sy2ogebAW Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Unsupervised_Neural_Machine_Translation Summary]
 
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|Nov 27 ||Xudong Peng  || 29 || DropBlock: A regularization method for convolutional networks || [https://arxiv.org/abs/1810.12890 Paper] ||  
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|Nov 27 ||Xudong Peng  || 29 || Visual Reinforcement Learning with Imagined Goals || [https://arxiv.org/abs/1807.04742 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Visual_Reinforcement_Learning_with_Imagined_Goals Summary]
 
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|Nov 27 ||Xinyue Zhang  || 30 || An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] ||  
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|Nov 27 ||Xinyue Zhang  || 30 || Policy Optimization with Demonstrations || [http://proceedings.mlr.press/v80/kang18a/kang18a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=policy_optimization_with_demonstrations Summary]
 
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|NOv 29 ||Junyi Zhang  ||  31 ||  Autoregressive Convolutional Neural Networks for Asynchronous Time Series  || [http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf Paper] ||
 
 
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|Nov 29 ||Travis Bender   || 32 || Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper] ||
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|NOv 29 ||Junyi Zhang   || 31 || Autoregressive Convolutional Neural Networks for Asynchronous Time Series  || [http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Autoregressive_Convolutional_Neural_Networks_for_Asynchronous_Time_Series Summary]
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[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:SOCNN.pdf Slides]
 
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|Nov 29 ||Patrick Li  || 33 || Matrix Capsules with EM Routing || [https://openreview.net/pdf?id=HJWLfGWRb Paper]  ||
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|Nov 29 ||Travis Bender  || 32 || ShakeDrop Regularization || [https://arxiv.org/pdf/1802.02375.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ShakeDrop_Regularization Summary] [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:ShakeDrop_Regularization.pdf Slides]
 
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|Makeup || Jiazhen Chen || 34 ||  ||   ||  
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|Nov 29 ||Patrick Li || 33 || Dynamic Routing Between Capsules || [https://arxiv.org/pdf/1710.09829.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CapsuleNets Summary] [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:STAT946_Presentation1.pdf Slides]||
 
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|Nov 30 || Gaurav Sahu || 35 || TBD || ||
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|Nov 30 || Jiazhen Chen || 34 || Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning || [https://arxiv.org/abs/1809.02121 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=learn_what_not_to_learn Summary]
 
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|Nov 23 || Kashif Khan || 36 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper] ||
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|Nov 30 || Gaurav Sahu  || 35 || Fix your classifier: the marginal value of training the last weight layer || [https://openreview.net/pdf?id=S1Dh8Tg0- Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Fix_your_classifier:_the_marginal_value_of_training_the_last_weight_layer Summary]
 
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|Nov 23 || Shala Chen  || 37 || A NEURAL REPRESENTATION OF SKETCH DRAWINGS ||  ||
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|Nov 23 || Kashif Khan || 36 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Wasserstein_Auto-encoders Summary]
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|Nov 23 || Shala Chen  || 37 || A Neural Representation of Sketch Drawings || [https://arxiv.org/pdf/1704.03477.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_neural_representation_of_sketch_drawings Summary]
 
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|Nov 30 || Ki Beom Lee || 38 || Detecting Statistical Interactions from Neural Network Weights|| [https://openreview.net/forum?id=ByOfBggRZ Paper] ||
 
|Nov 30 || Ki Beom Lee || 38 || Detecting Statistical Interactions from Neural Network Weights|| [https://openreview.net/forum?id=ByOfBggRZ Paper] ||
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[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DETECTING_STATISTICAL_INTERACTIONS_FROM_NEURAL_NETWORK_WEIGHTS Summary]
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|Nov 23 || Wesley Fisher || 39 || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Reinforcement_Learning_in_Continuous_Action_Spaces_a_Case_Study_in_the_Game_of_Simulated_Curling Summary]
 
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|Makeup || Wesley Fisher || 39 || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Reinforcement_Learning_in_Continuous_Action_Spaces_a_Case_Study_in_the_Game_of_Simulated_Curling Summary]||
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||Nov 30|| Ahmed Afify || 40 ||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ  Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DON'T_DECAY_THE_LEARNING_RATE_,_INCREASE_THE_BATCH_SIZE Summary]
 
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||Nov 30|| Ahmed Afify || 40 ||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ  Paper]||
 

Latest revision as of 03:22, 2 December 2018

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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 Slides
Nov 6 Aravind Ravi 11 Towards Image Understanding from Deep Compression Without Decoding Paper Summary

Slides

Nov 6 Ronald Feng 12 Learning to Teach Paper Summary

Slides

Nov 8 Neel Bhatt 13 Annotating Object Instances with a Polygon-RNN Paper Summary Slides
Nov 8 Jacob Manuel 14 Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels Paper Summary Slides
Nov 8 Charupriya Sharma 15 A Bayesian Perspective on Generalization and Stochastic Gradient Descent 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 Summary
Nov 13 Neil Budnarain 18 Predicting Floor Level For 911 Calls with Neural Networks and Smartphone Sensor Data Paper Summary
NOv 15 Zheng Ma 19 Reinforcement Learning of Theorem Proving Paper Summary Slides
Nov 15 Abdul Khader Naik 20 Multi-View Data Generation Without View Supervision Paper Summary
Nov 15 Johra Muhammad Moosa 21 Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin Paper Summary Slides
NOv 20 Zahra Rezapour Siahgourabi 22 Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias Paper

Summary

Nov 20 Shubham Koundinya 23 Countering Adversarial Images Using Input Transformations paper

Summary Slides

Nov 20 Salman Khan 24 Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples paper Summary
NOv 22 Soroush Ameli 25 Learning to Navigate in Cities Without a Map paper Summary
Nov 22 Ivan Li 26 Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction Paper Summary
Nov 22 Sigeng Chen 27 Conditional Neural Processes Paper Summary
Nov 27 Aileen Li 28 Unsupervised Neural Machine Translation Paper Summary
Nov 27 Xudong Peng 29 Visual Reinforcement Learning with Imagined Goals Paper Summary
Nov 27 Xinyue Zhang 30 Policy Optimization with Demonstrations Paper Summary
NOv 29 Junyi Zhang 31 Autoregressive Convolutional Neural Networks for Asynchronous Time Series Paper Summary

Slides

Nov 29 Travis Bender 32 ShakeDrop Regularization Paper Summary Slides
Nov 29 Patrick Li 33 Dynamic Routing Between Capsules Paper Summary Slides
Nov 30 Jiazhen Chen 34 Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning Paper Summary
Nov 30 Gaurav Sahu 35 Fix your classifier: the marginal value of training the last weight layer Paper Summary
Nov 23 Kashif Khan 36 Wasserstein Auto-Encoders Paper Summary
Nov 23 Shala Chen 37 A Neural Representation of Sketch Drawings Paper Summary
Nov 30 Ki Beom Lee 38 Detecting Statistical Interactions from Neural Network Weights Paper

Summary

Nov 23 Wesley Fisher 39 Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling Paper Summary
Nov 30 Ahmed Afify 40 Don't Decay the Learning Rate, Increase the Batch Size Paper Summary