# Difference between revisions of "stat946F18"

From statwiki

(→Paper presentation) |
(→Record your contributions here [https://docs.google.com/spreadsheets/d/1SxkjNfhOg_eXWpUnVHuIP93E6tEiXEdpm68dQGencgE/edit?usp=sharing]) |
||

(149 intermediate revisions by 41 users not shown) | |||

Line 2: | Line 2: | ||

=Paper presentation= | =Paper presentation= | ||

+ | |||

+ | [https://goo.gl/forms/8NucSpF36K6IUZ0V2 Your feedback on presentations] | ||

+ | |||

+ | |||

+ | = Record your contributions here [https://docs.google.com/spreadsheets/d/1SxkjNfhOg_eXWpUnVHuIP93E6tEiXEdpm68dQGencgE/edit?usp=sharing]= | ||

+ | |||

+ | 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). | ||

+ | |||

+ | |||

+ | |||

+ | |||

+ | |||

+ | |||

{| class="wikitable" | {| class="wikitable" | ||

Line 17: | Line 36: | ||

|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] | ||

+ | [https://wiki.math.uwaterloo.ca/statwiki/images/e/ea/Beyond_Word_Importance.pdf Slides] | ||

|- | |- | ||

|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] || | ||

− | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title= | + | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DCN_plus:_Mixed_Objective_And_Deep_Residual_Coattention_for_Question_Answering Summary] |

|- | |- | ||

|Oct 25 || Juan Carrillo || 3 || Hierarchical Representations for Efficient Architecture Search || [https://arxiv.org/abs/1711.00436 Paper] || | |Oct 25 || Juan Carrillo || 3 || Hierarchical Representations for Efficient Architecture Search || [https://arxiv.org/abs/1711.00436 Paper] || | ||

[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary] | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary] | ||

+ | [https://wiki.math.uwaterloo.ca/statwiki/images/1/15/HierarchicalRep-slides.pdf Slides] | ||

+ | |- | ||

+ | |Oct 30 || Manpreet Singh Minhas || 4 || End-to-end Active Object Tracking via Reinforcement Learning || [http://proceedings.mlr.press/v80/luo18a/luo18a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=End_to_end_Active_Object_Tracking_via_Reinforcement_Learning Summary] | ||

+ | |- | ||

+ | |Oct 30 || Marvin Pafla || 5 || Fairness Without Demographics in Repeated Loss Minimization || [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Fairness_Without_Demographics_in_Repeated_Loss_Minimization Summary] | ||

|- | |- | ||

− | |Oct 30 || | + | |Oct 30 || Glen Chalatov || 6 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding Paper] || |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pixels_to_Graphs_by_Associative_Embedding Summary] | ||

|- | |- | ||

− | | | + | |Nov 1 || Sriram Ganapathi Subramanian || 7 ||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/differentiableplasticity Summary] |

+ | [https://wiki.math.uwaterloo.ca/statwiki/images/3/3c/Deep_learning_course_presentation.pdf Slides] | ||

|- | |- | ||

− | | | + | |Nov 1 || Hadi Nekoei || 8 || Synthesizing Programs for Images using Reinforced Adversarial Learning || [http://proceedings.mlr.press/v80/ganin18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Synthesizing_Programs_for_Images_usingReinforced_Adversarial_Learning Summary] |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Synthesizing_Programs_for_Images_using_Reinforced_Adversarial_Learning.pdf Slides] | ||

|- | |- | ||

− | |Nov 1 || | + | |Nov 1 || Henry Chen || 9 || DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] || |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DeepVO_Towards_end_to_end_visual_odometry_with_deep_RNN Summary] | ||

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:DeepVO_Presentation_Henry.pdf Slides] | ||

|- | |- | ||

− | |Nov | + | |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 | + | |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] | ||

|- | |- | ||

− | |Nov 6 || | + | |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] |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:946_L2T_slides.pdf Slides] | ||

|- | |- | ||

− | |Nov | + | |Nov 8 || Neel Bhatt || 13 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Annotating_Object_Instances_with_a_Polygon_RNN Summary] [https://wiki.math.uwaterloo.ca/statwiki/images/a/af/ANNOTATING_OBJECT_INSTANCES_NEEL_BHATT.pdf Slides] |

|- | |- | ||

− | |Nov | + | |Nov 8 || Jacob Manuel || 14 || Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels || [https://arxiv.org/pdf/1804.06872.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Co-Teaching Summary] [https://wiki.math.uwaterloo.ca/statwiki/images/3/33/Co-Teaching.pdf Slides] |

|- | |- | ||

− | |Nov 8 || | + | |Nov 8 || Charupriya Sharma|| 15 || A Bayesian Perspective on Generalization and Stochastic Gradient Descent|| [https://openreview.net/pdf?id=BJij4yg0Z Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=A_Bayesian_Perspective_on_Generalization_and_Stochastic_Gradient_Descent Summary] |

|- | |- | ||

− | | | + | |NOv 13 || Sagar Rajendran || 16 || Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Zero-Shot_Visual_Imitation Summary] |

|- | |- | ||

− | |Nov | + | |

+ | |Nov 13 || Ruijie Zhang || 17 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Searching_For_Efficient_Multi_Scale_Architectures_For_Dense_Image_Prediction 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] |

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | |Nov | + | |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 || 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] |

|- | |- | ||

− | | | + | |NOv 20 || Zahra Rezapour Siahgourabi || 22 ||Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias ||[https://arxiv.org/pdf/1807.07049 Paper] || |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Robot_Learning_in_Homes:_Improving_Generalization_and_Reducing_Dataset_Bias Summary] | ||

|- | |- | ||

− | |Nov | + | |Nov 20 || Shubham Koundinya || 23 || Countering Adversarial Images Using Input Transformations ||[https://openreview.net/pdf?id=SyJ7ClWCb paper] || |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Countering_Adversarial_Images_Using_Input_Transformations Summary] | ||

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:Countering_Adversarial_Images.pdf Slides] | ||

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | |Nov | + | |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] |

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | |Nov | + | |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] |

|- | |- | ||

− | |Nov | + | |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] |

|- | |- | ||

− | |Nov 27 || | + | |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] |

|- | |- | ||

− | |||

|- | |- | ||

− | | | + | |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] |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=File:SOCNN.pdf Slides] | ||

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | |Nov 29 || | + | |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]|| |

|- | |- | ||

− | |Nov | + | |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] |

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | | | + | |Nov 30 || Ki Beom Lee || 38 || Detecting Statistical Interactions from Neural Network Weights|| [https://openreview.net/forum?id=ByOfBggRZ Paper] || |

+ | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DETECTING_STATISTICAL_INTERACTIONS_FROM_NEURAL_NETWORK_WEIGHTS Summary] | ||

|- | |- | ||

− | | | + | |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] |

|- | |- | ||

− | | | + | ||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] |

|- | |- | ||

− |

## Latest revision as of 03:22, 2 December 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 | ||

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 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 | ||

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

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 | |

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 | ||

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 |