stat946F18: Difference between revisions
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[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] | ||
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|Oct 30 || Manpreet Singh Minhas || | |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] | ||
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|Oct 30 || Marvin Pafla || | |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] | ||
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|Oct 30 || Glen Chalatov || | |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] | [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pixels_to_Graphs_by_Associative_Embedding Summary] | ||
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|Nov 1 || Sriram Ganapathi Subramanian || | |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] | ||
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|Nov 1 || Hadi Nekoei || | |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] | ||
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|Nov 1 || Henry Chen || | |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] || | ||
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|Nov 6 || Nargess Heydari || | |Nov 6 || Nargess Heydari || 10 ||Wavelet Pooling For Convolutional Neural Networks Networks || [https://openreview.net/pdf?id=rkhlb8lCZ Paper] || | ||
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|Nov 6 || Aravind Ravi || | |Nov 6 || Aravind Ravi || 11 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] || | ||
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|Nov 6 || Ronald Feng || | |Nov 6 || Ronald Feng || 12 || Learning to Teach || [https://openreview.net/pdf?id=HJewuJWCZ Paper] || | ||
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|Nov 8 || Neel Bhatt || | |Nov 8 || Neel Bhatt || 13 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] || | ||
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|Nov 8 || Jacob Manuel || | |Nov 8 || Jacob Manuel || 14 || || || | ||
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|Nov 8 || Charupriya Sharma|| | |Nov 8 || Charupriya Sharma|| 15 || || || | ||
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|NOv 13 || Sagar Rajendran || | |NOv 13 || Sagar Rajendran || 16 || Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] || | ||
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|Nov 13 || Jiazhen Chen || | |Nov 13 || Jiazhen Chen || 17 || || || | ||
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|Nov 13 || Neil Budnarain || | |Nov 13 || Neil Budnarain || 18 || PixelNN: Example-Based Image Synthesis || [https://openreview.net/pdf?id=Syhr6pxCW Paper] || | ||
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|NOv 15 || Zheng Ma || | |NOv 15 || Zheng Ma || 19 || Reinforcement Learning of Theorem Proving || [https://arxiv.org/abs/1805.07563 Paper] || | ||
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|Nov 15 || Abdul Khader Naik || | |Nov 15 || Abdul Khader Naik || 20 || || || | ||
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|Nov 15 || Johra Muhammad Moosa || | |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] || | ||
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|NOv 20 || Zahra Rezapour Siahgourabi || | |NOv 20 || Zahra Rezapour Siahgourabi || 22 || || || | ||
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|Nov 20 || Shubham Koundinya || | |Nov 20 || Shubham Koundinya || 23 || || || | ||
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|Nov 20 || Salman Khan || | |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 22 ||Soroush Ameli || | |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 ||Ivan Li || | |Nov 22 ||Ivan Li || 26 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] || | ||
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|Nov 22 ||Sigeng Chen || | |Nov 22 ||Sigeng Chen || 27 || || || | ||
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|Nov 27 || Aileen Li || | |Nov 27 || Aileen Li || 28 || Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper] || | ||
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|NOv 27 ||Xudong Peng || | |NOv 27 ||Xudong Peng || 29 || Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] || | ||
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|Nov 27 ||Xinyue Zhang || | |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 29 ||Junyi Zhang || | |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 || | |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 ||Patrick Li || | |Nov 29 ||Patrick Li || 33 || Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper] || | ||
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| | |Makeup || Ruijie Zhang || 34 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]|| | ||
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| | |Makeup || Ahmed Afify || 35 ||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ Paper]|| | ||
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| | |Makeup || Gaurav Sahu || 36 || TBD || || | ||
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| | |Makeup || Kashif Khan || 37 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper] || | ||
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| | |Makeup || Shala Chen || 38 || A NEURAL REPRESENTATION OF SKETCH DRAWINGS || || | ||
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| | |Makeup || Ki Beom Lee || 39 || || || | ||
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| | |Makeup || Wesley Fisher || 40 || 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] |
Revision as of 10:55, 25 October 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 | |
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 | |
Nov 6 | Aravind Ravi | 11 | Towards Image Understanding from Deep Compression Without Decoding | Paper | |
Nov 6 | Ronald Feng | 12 | Learning to Teach | Paper | |
Nov 8 | Neel Bhatt | 13 | Annotating Object Instances with a Polygon-RNN | Paper | |
Nov 8 | Jacob Manuel | 14 | |||
Nov 8 | Charupriya Sharma | 15 | |||
NOv 13 | Sagar Rajendran | 16 | Zero-Shot Visual Imitation | Paper | |
Nov 13 | Jiazhen Chen | 17 | |||
Nov 13 | Neil Budnarain | 18 | PixelNN: Example-Based Image Synthesis | Paper | |
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 | Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate | 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 | Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices | Paper | |
Makeup | Ruijie Zhang | 34 | Searching for Efficient Multi-Scale Architectures for Dense Image Prediction | Paper | |
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 | |||
Makeup | Wesley Fisher | 40 | Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling | Paper | Summary |