http://wiki.math.uwaterloo.ca/statwiki/api.php?action=feedcontributions&user=Aghodsib&feedformat=atomstatwiki - User contributions [US]2021-12-05T07:42:50ZUser contributionsMediaWiki 1.28.3http://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=49935stat441F212021-09-27T17:24:33Z<p>Aghodsib: /* Project Proposal */</p>
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== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
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<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
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=Paper presentation=<br />
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|width="60pt"|Date<br />
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|width="15pt"|Paper number <br />
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|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
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|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
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|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=49933stat441F212021-09-10T15:30:04Z<p>Aghodsib: </p>
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<div><br />
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== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
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=Paper presentation=<br />
{| class="wikitable"<br />
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|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
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|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
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|Week of Nov 16 || Ali Ghodsi || || || || ||<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49932stat940F212021-09-10T14:27:27Z<p>Aghodsib: </p>
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<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
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=Paper presentation=<br />
{| class="wikitable"<br />
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|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the video<br />
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|Week of Nov 8 || Abhinav Chanana (Example) || 1||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://youtu.be/epBzlXHFNlY Presentation ]<br />
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|Week of Nov 11 || || || || ||<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=49931stat441F212021-09-06T02:57:45Z<p>Aghodsib: </p>
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<div><br />
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== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
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=Paper presentation=<br />
{| class="wikitable"<br />
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{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
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|Week of Nov 16 || || || || || ||<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=49930stat441F212021-09-06T02:55:20Z<p>Aghodsib: /* Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing] */</p>
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<div><br />
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== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
[https://www.youtube.com/watch?v=TVLpSFYgF0c&feature=youtu.be]<br />
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|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Influenza_Forecasting_Framework_based_on_Gaussian_Processes Summary]|| [https://www.youtube.com/watch?v=HZG9RAHhpXc&feature=youtu.be]<br />
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|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Gtompkin Summary] || [https://learn.uwaterloo.ca/d2l/ext/rp/577051/lti/framedlaunch/6ec1ebea-5547-46a2-9e4f-e3dc9d79fd54]<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification Summary] || [https://www.youtube.com/watch?v=UgVRzi9Ubws]<br />
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|Week of Nov 16 || Matthew Hall, Johnathan Chalaturnyk || 5|| Neural Ordinary Differential Equations || [https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_ODEs Summary]||<br />
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|Week of Nov 16 || Luwen Chang, Qingyang Yu, Tao Kong, Tianrong Sun || 6|| Adversarial Attacks on Copyright Detection Systems || Paper [https://proceedings.icml.cc/static/paper_files/icml/2020/1894-Paper.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Attacks_on_Copyright_Detection_Systems Summary] || [https://www.youtube.com/watch?v=bQI9S6bCo8o]<br />
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|Week of Nov 16 || Casey De Vera, Solaiman Jawad || 7|| IPBoost – Non-Convex Boosting via Integer Programming || [https://arxiv.org/pdf/2002.04679.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=IPBoost Summary] || [https://www.youtube.com/watch?v=4DhJDGC4pyI&feature=youtu.be]<br />
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|Week of Nov 16 || Yuxin Wang, Evan Peters, Yifan Mou, Sangeeth Kalaichanthiran || 8|| What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games || [https://arxiv.org/pdf/1805.02777.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=what_game_are_we_playing Summary] || [https://www.youtube.com/watch?v=9qJoVxo3hnI&feature=youtu.be]<br />
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|Week of Nov 16 || Yuchuan Wu || 9|| || || ||<br />
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|Week of Nov 16 || Zhou Zeping, Siqi Li, Yuqin Fang, Fu Rao || 10|| A survey of neural network-based cancer prediction models from microarray data || [https://www.sciencedirect.com/science/article/pii/S0933365717305067 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Y93fang Summary] || [https://youtu.be/B8pPUU8ypO0]<br />
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|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:J46hou Summary] || [https://www.youtube.com/watch?v=tvCEvvy54X8&ab_channel=JJLian]<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Networks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Cvmustat Summary] || [https://www.youtube.com/watch?v=or5RTxDnZDo]<br />
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|Week of Nov 23 || Taohao Wang, Zeren Shen, Zihao Guo, Rui Chen || 13|| Large Scale Landmark Recognition via Deep Metric Learning || [https://arxiv.org/pdf/1908.10192.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:T358wang Summary] || [https://www.youtube.com/watch?v=K9NypDNPLJo Video]<br />
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|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Task_Understanding_from_Confusing_Multi-task_Data Summary] || [https://youtu.be/i_5PQdfuH-Y]<br />
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|Week of Nov 23 || Rui Gong, Xuetong Wang, Xinqi Ling, Di Ma || 15|| Semantic Relation Classification via Convolution Neural Network|| [https://www.aclweb.org/anthology/S18-1127.pdf paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Semantic_Relation_Classification——via_Convolution_Neural_Network Summary]|| [https://www.youtube.com/watch?v=m9o3NuMUKkU&ab_channel=DiMa video]<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| Graph Structure of Neural Networks || [https://proceedings.icml.cc/paper/2020/file/757b505cfd34c64c85ca5b5690ee5293-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Graph_Structure_of_Neural_Networks Summary] || [https://youtu.be/x9eUgEwntcs Video]<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Superhuman AI for multiplayer poker || [https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Superhuman_AI_for_Multiplayer_Poker Summary]|| [https://www.youtube.com/watch?v=kazqcOwbtTI Video]<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence Summary] || [https://www.youtube.com/watch?v=aAwjaos_Hus Video]<br />
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|Week of Nov 23 || Jerry Huang, Daniel Jiang, Minyan Dai || 19|| Neural Speed Reading Via Skim-RNN ||[https://arxiv.org/pdf/1711.02085.pdf?fbclid=IwAR3EeFsKM_b5p9Ox7X9mH-1oI3U3oOKPBy3xUOBN0XvJa7QW2ZeJJ9ypQVo Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_Speed_Reading_via_Skim-RNN Summary]|| [https://youtu.be/vOENmt9jgVE Video]<br />
|-<br />
|Week of Nov 23 ||Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew || 20|| DivideMix: Learning with Noisy Labels as Semi-supervised Learning || [https://openreview.net/pdf?id=HJgExaVtwr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Yktan Summary]|| [https://www.youtube.com/watch?v=48xYZXifjS0&ab_channel=SeakraChin]<br />
|-<br />
|Week of Nov 30 || Banno Dion, Battista Joseph, Kahn Solomon || 21|| Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks || [https://www.sciencedirect.com/science/article/pii/S1877050919310646] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Music_Recommender_System_Based_using_CRNN#Evaluation_of_Music_Recommendation_System: Summary] || [https://youtu.be/eGUV3zwLwqQ]<br />
|-<br />
|Week of Nov 30 || Isaac Ellmen, Dorsa Mohammadrezaei, Emilee Carson || 22|| A universal SNP and small-indel variant caller using deep neural networks||[https://www.nature.com/articles/nbt.4235.epdf?author_access_token=q4ZmzqvvcGBqTuKyKgYrQ9RgN0jAjWel9jnR3ZoTv0NuM3saQzpZk8yexjfPUhdFj4zyaA4Yvq0LWBoCYQ4B9vqPuv8e2HHy4vShDgEs8YxI_hLs9ov6Y1f_4fyS7kGZ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=A_universal_SNP_and_small-indel_variant_caller_using_deep_neural_networks Summary] ||<br />
|-<br />
|Week of Nov 30 || Daniel Fagan, Cooper Brooke, Maya Perelman || 23|| Efficient kNN Classification With Different Number of Nearest Neighbors || [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898482 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Dfagan Summary]|| [https://youtu.be/_STVyvm_Kao]<br />
|-<br />
|Week of Nov 30 || Karam Abuaisha, Evan Li, Jason Pu, Nicholas Vadivelu || 24|| Being Bayesian about Categorical Probability || [https://proceedings.icml.cc/static/paper_files/icml/2020/3560-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Being_Bayesian_about_Categorical_Probability Summary] || [https://drive.google.com/file/d/1I0uYF2xEMuNVtaEhPb_vZ6bxSKMi0gUh/view?usp=sharing]<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Loss_Function_Search_for_Face_Recognition] || [https://youtu.be/i3dXnK9HGSQ]<br />
|-<br />
|Week of Nov 30 ||Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee || 26|| Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms || [https://arxiv.org/pdf/1912.07618.pdf?fbclid=IwAR0RwATSn4CiT3qD9LuywYAbJVw8YB3nbex8Kl19OCExIa4jzWaUut3oVB0 Paper] || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&fbclid=IwAR1Tad2DAM7LT6NXXuSYDZtHHBvN0mjZtDdCOiUFFq_XwVcQxG3hU-3XcaE] || [https://www.youtube.com/watch?v=kiYbAmd_3IA]<br />
|-<br />
|Week of Nov 30 || Stan Lee, Seokho Lim, Kyle Jung, Dae Hyun Kim || 27|| Improving neural networks by preventing co-adaption of feature detectors || [https://arxiv.org/pdf/1207.0580.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Improving_neural_networks_by_preventing_co-adaption_of_feature_detectors Summary] || [https://youtu.be/SV5UOM3QwiA Video]<br />
|-<br />
|Week of Nov 30 || Yawen Wang, Danmeng Cui, ZiJie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid || 28|| A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques || [https://arxiv.org/pdf/1707.02919.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining Summary] || [https://youtu.be/to1fj0GyTAg]<br />
|-<br />
|Week of Nov 30 || Qing Guo, XueGuang Ma, James Ni, Yuanxin Wang || 29|| Mask R-CNN || [https://arxiv.org/pdf/1703.06870.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Mask_RCNN Summary] || [https://youtu.be/NgcSMXNDNuU]<br />
|-<br />
|Week of Nov 30 || Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_Papers_Classification_System Summary] || [https://youtu.be/Ug-5H4B5xkQ]<br />
|-<br />
|Week of Nov 30 || Daniel Zhang, Jacky Yao, Scholar Sun, Russell Parco, Ian Cheung || 31 || Speech2Face: Learning the Face Behind a Voice || [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Speech2Face:_Learning_the_Face_Behind_a_Voice Summary] || [https://youtu.be/lNQAbMxOj4w]<br />
|-<br />
|Week of Nov 30 || Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du || 32 || Evaluating Machine Accuracy on ImageNet || [https://proceedings.icml.cc/static/paper_files/icml/2020/6173-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Evaluating_Machine_Accuracy_on_ImageNet Summary] || [https://youtu.be/jj4S3VGzQz4 Video]<br />
|-<br />
|Week of Nov 30 || Mushi Wang, Siyuan Qiu, Yan Yu || 33 || Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections || [https://ieeexplore.ieee.org/abstract/document/8957421 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Surround_Vehicle_Motion_Prediction Summary] || [https://youtu.be/cqyn3aO_5tc Video 33]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=49929F21-STAT 441/841 CM 763-Proposal2021-09-06T02:54:53Z<p>Aghodsib: Replaced content with "Use this format (Don’t remove Project 0) Project # 0 Group members: Last name, First name Last name, First name Last name, First name Last name, First name Title:..."</p>
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<div>Use this format (Don’t remove Project 0)<br />
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Project # 0 Group members:<br />
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Last name, First name<br />
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Title: Making a String Telephone<br />
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Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
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--------------------------------------------------------------------</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_441/841_CM_763-Proposal&diff=49927F21-STAT 441/841 CM 763-Proposal2021-09-06T02:54:25Z<p>Aghodsib: Aghodsib moved page F20-STAT 441/841 CM 763-Proposal to F21-STAT 441/841 CM 763-Proposal</p>
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<div>Use this format (Don’t remove Project 0)<br />
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Project # 0 Group members:<br />
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Last name, First name<br />
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Last name, First name<br />
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Last name, First name<br />
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Last name, First name<br />
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Title: Making a String Telephone<br />
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Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
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'''Project # 1 Group members:'''<br />
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Song, Quinn<br />
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Loh, William<br />
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Bai, Junyue<br />
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Choi, Phoebe<br />
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'''Title:''' APTOS 2019 Blindness Detection<br />
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'''Description:'''<br />
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Our team chose the APTOS 2019 Blindness Detection Challenge from Kaggle. The goal of this challenge is to build a machine learning model that detects diabetic retinopathy by screening retina images.<br />
<br />
Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working-aged adults. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). In rural areas where medical screening is difficult to conduct, it is challenging to detect the disease efficiently. Aravind Eye Hospital hopes to utilize machine learning techniques to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.<br />
<br />
Our team plans to solve this problem by applying our knowledge in image processing and classification.<br />
<br />
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<br />
'''Project # 2 Group members:'''<br />
<br />
Li, Dylan<br />
<br />
Li, Mingdao<br />
<br />
Lu, Leonie<br />
<br />
Sharman,Bharat<br />
<br />
'''Title:''' Risk prediction in life insurance industry using supervised learning algorithms<br />
<br />
'''Description:'''<br />
<br />
In this project, we aim to replicate and possibly improve upon the work of Jayabalan et al. in their paper “Risk prediction in life insurance industry using supervised learning algorithms”. We will be using the Prudential Life Insurance Data Set that the authors have used and have shared with us. We will be pre-processing the data to replace missing values, using feature selection using CFS and feature reduction using PCA use this processed data to perform Classification via four algorithms – Neural Networks, Random Tree, REPTree and Multiple Linear Regression. We will compare the performance of these Algorithms using MAE and RMSE metrics and come up with visualizations that can explain the results easily even to a non-quantitative audience. <br />
<br />
Our goal behind this project is to learn applying the algorithms that we learned in our class to an industry dataset and come up with results that we can aid better, data-driven decision making.<br />
<br />
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<br />
'''Project # 3 Group members:'''<br />
<br />
Parco, Russel<br />
<br />
Sun, Scholar<br />
<br />
Yao, Jacky<br />
<br />
Zhang, Daniel<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles<br />
<br />
'''Description:''' <br />
<br />
Our team has decided to participate in the Lyft Motion Prediction for Autonomous Vehicles Kaggle competition. The aim of this competition is to build a model which given a set of objects on the road (pedestrians, other cars, etc), predict the future movement of these objects.<br />
<br />
Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. However, there are still significant engineering challenges to be solved before one can fully realize the benefits of self-driving cars. One such challenge is building models that reliably predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians.<br />
<br />
Our aim is to apply classification techniques learned in class to optimally predict how these objects move.<br />
<br />
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<br />
'''Project # 4 Group members:'''<br />
<br />
Chow, Jonathan<br />
<br />
Dharani, Nyle<br />
<br />
Nasirov, Ildar<br />
<br />
'''Title:''' Classification with Abstinence<br />
<br />
'''Description:''' <br />
<br />
We seek to implement the algorithm described in [https://papers.nips.cc/paper/9247-deep-gamblers-learning-to-abstain-with-portfolio-theory.pdf Deep Gamblers: Learning to Abstain with Portfolio Theory]. The paper describes augmenting classification problems to include the option of abstaining from making a prediction when confidence is low.<br />
<br />
Medical imaging diagnostics is a field in which classification could assist professionals and improve life expectancy for patients through increased accuracy. However, there are also severe consequences to incorrect predictions. As such, we also hope to apply the algorithm implemented to the classification of medical images, specifically instances of normal and pneumonia [https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia? chest x-rays]. <br />
<br />
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<br />
'''Project # 5 Group members:'''<br />
<br />
Jones, Hayden<br />
<br />
Leung, Michael<br />
<br />
Haque, Bushra<br />
<br />
Mustatea, Cristian<br />
<br />
'''Title:''' Combine Convolution with Recurrent Networks for Text Classification<br />
<br />
'''Description:''' <br />
<br />
Our team chose to reproduce the paper [https://arxiv.org/pdf/2006.15795.pdf Combine Convolution with Recurrent Networks for Text Classification] on Arxiv. The goal of this paper is to combine CNN and RNN architectures in a way that more flexibly combines the output of both architectures other than simple concatenation through the use of a “neural tensor layer” for the purpose of improving at the task of text classification. In particular, the paper claims that their novel architecture excels at the following types of text classification: sentiment analysis, news categorization, and topical classification. Our team plans to recreate this paper by working in pairs of 2, one pair to implement the CNN pipeline and the other pair to implement the RNN pipeline. We will be working with Tensorflow 2, Google Collab, and reproducing the paper’s experimental results with training on the same 6 publicly available datasets found in the paper.<br />
<br />
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<br />
'''Project # 6 Group members:'''<br />
<br />
Chin, Ruixian<br />
<br />
Ong, Jason<br />
<br />
Chiew, Wen Cheen<br />
<br />
Tan, Yan Kai<br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction<br />
<br />
'''Description:''' <br />
<br />
Our team chose to participate in a Kaggle research challenge "Mechanisms of Action (MoA) Prediction". This competition is a project within the Broad Institute of MIT and Harvard, the Laboratory for Innovation Science at Harvard (LISH), and the NIH Common Funds Library of Integrated Network-Based Cellular Signatures (LINCS), present this challenge with the goal of advancing drug development through improvements to MoA prediction algorithms.<br />
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<br />
'''Project # 7 Group members:'''<br />
<br />
Ren, Haotian <br />
<br />
Cheung, Ian Long Yat<br />
<br />
Hussain, Swaleh <br />
<br />
Zahid, Bin, Haris <br />
<br />
'''Title:''' Transaction Fraud Detection <br />
<br />
'''Description:''' <br />
<br />
Protecting people from fraudulent transactions is an important topic for all banks and internet security companies. This Kaggle project is based on the dataset from IEEE Computational Intelligence Society (IEEE-CIS). Our objective is to build a more efficient model in order to recognize each fraud transaction with a higher accuracy and higher speed.<br />
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<br />
'''Project # 8 Group members:'''<br />
<br />
ZiJie, Jiang<br />
<br />
Yawen, Wang<br />
<br />
DanMeng, Cui<br />
<br />
MingKang, Jiang<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles <br />
<br />
'''Description:'''<br />
<br />
Our team chose to participate in the Kaggle Challenge "Lyft Motion Prediction for Autonomous Vehicles". We will apply our science skills to build motion prediction models for self-driving vehicles. The model will be able to predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians. The goal of this competition is to predict the trajectories of other traffic participants.<br />
<br />
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<br />
<br />
'''Project # 9 Group members:'''<br />
<br />
Banno, Dion <br />
<br />
Battista, Joseph<br />
<br />
Kahn, Solomon <br />
<br />
'''Title:''' Increasing Spotify user engagement through predictive personalization<br />
<br />
'''Description:''' <br />
<br />
Our project is an application of classification to the domain of predictive personalization. The goal of the project is to increase Spotify user engagement through data-driven methods. Given a set of users’ demographic data, listening preferences and behaviour, our goal is to build a recommendation system that suggests new songs to users. From a potential pool of songs to suggest, the final song recommendations will be driven by a classification algorithm that measures a given user’s propensity to like a song. We plan on leveraging the Spotify Web API to gather data about songs and collecting user data from consenting peers.<br />
<br />
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<br />
'''Project # 10 Group members:'''<br />
<br />
Qing, Guo <br />
<br />
Wang, Yuanxin<br />
<br />
James, Ni<br />
<br />
Xueguang, Ma<br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction<br />
<br />
'''Description:''' <br />
<br />
Our team has decided to participate in the Mechanisms of Action (MoA) Prediction Kaggle competition. This is a challenge with the goal of advancing drug development through improvements to MoA prediction algorithms.<br />
Our team plan to develop an algorithm to predict a compound’s MoA given its cellular signature and our goal is to learn various algorithms taught in this course.<br />
<br />
<br />
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<br />
'''Project # 11 Group members:'''<br />
<br />
Yang, Jiwon <br />
<br />
Mahdi, Anas<br />
<br />
Thibault, Will<br />
<br />
Lau, Jan<br />
<br />
'''Title:''' Application of classification in human fatigue analysis<br />
<br />
'''Description:''' <br />
<br />
The goal of this project is to classify different levels of fatigue based on motion capture (Vicon) and force plates data. First, we plan to obtain data from 4 to 6 participants performing squats or squats with weights and rate them on a fatigue scale, with each participant doing at least 50 to 100 reps. We will collect data with EMG, IMU, force plates, and Vicon. When the participants are squatting, we will ask them about their fatigue level, and compare their feedback against the fatigue level recorded on EMG. The fatigue level will be on a scale of 1 to 10 (1 being not fatigued at all and 10 being cannot continue anymore). Once data is collected, we will classify the motion capture and force plates data into the different levels of fatigue.<br />
<br />
<br />
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<br />
'''Project # 12 Group members:'''<br />
<br />
Xiaolan Xu, <br />
<br />
Robin Wen, <br />
<br />
Yue Weng, <br />
<br />
Beizhen Chang<br />
<br />
'''Title:''' Identification (Classification) of Submillimetre Galaxies Based on Multiwavelength Data in Astronomy<br />
<br />
'''Description:''' <br />
<br />
Identifying the counterparts of submillimetre galaxies (SMGs) in multiwavelength images is important to the study of galaxy evolution in astronomy. However, obtaining a statistically significant sample of robust associations is very challenging because of the poor angular resolution of single-dish submm facilities, that is we can not tell which galalxy is actually responsible for the submillimeter emission from a group of possible candidates due to the poor resolution. Recently, a set of labelled dataset is obtained from ALMA, a milliemetre/submilliemetre telescope array with the sufficient resolution to pin down the exact source of submillimeter emssion. However, applying such array to large fraction of skies are not feasible, so it is of practical interest to develop algorithm to identify submillimetre galaxies (SMGs) based on the other available data. With this newly labelled dataset from ALMA, it is possible to test and develop different new alrgorithms and apply them on unlabelled data to detect submillimetre galaxies.<br />
<br />
In our work, we primarily build on the works of Liu et al.(https://arxiv.org/abs/1901.09594), which tested a set of standard classification algorithms to the dataset. We aim to first reproduce their work and test other classification algorithms with a more stastics centered perspective. Next, we hope to possibly extend their works from one or some of the following directions: (1)Incorporating some other relevant features to augment the dimensions of the available dataset for better classification rate. (2)Taking the measurement error into the classifcation algorithms, possibly from a Bayesian approach. (All features in astronomy datasets come from actual physical measurements, which come with an error bar. However, it is not clear how to incoporate this error into the classification task.) (3)The possibility of combining some tradtional astronomy approaches with algorithms from ML.<br />
<br />
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<br />
'''Project # 13 Group members:'''<br />
<br />
<br />
Zihui (Betty) Qin,<br />
<br />
Wenqi (Maggie) Zhao,<br />
<br />
Muyuan Yang,<br />
<br />
Amartya (Marty) Mukherjee,<br />
<br />
'''Title:''' Insider Trading Roles Classification Prediction on United States conventional stock or non-derivative transaction<br />
<br />
'''Description:'''<br />
<br />
Background (why we were interested in classifying based on insiders): <br />
The United States is one of the most frequently traded financial markets in the world. The dataset captures all insider activities as reported on SEC (U.S. Securities and Exchange Commission) forms 3, 4, 5, and 144. We believe that using variables (such as transaction date, security type, and transaction amount), we could predict the roles code for a new transaction. The reason for the chosen prediction is that the role of the insider gives investors signals of potential internal activities and private information. This is crucial for investors to detect important market signals from those insider trading activities, such that they could benefit from the market. <br />
<br />
Goal: To classify the role of an insider in a company based on the data of their trades.<br />
<br />
<br />
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<br />
'''Project # 14 Group members:'''<br />
<br />
Jung, Kyle<br />
<br />
Kim, Dae Hyun<br />
<br />
Lee, Stan<br />
<br />
Lim, Seokho<br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction Competition<br />
<br />
'''Description:''' The main objective of this Kaggle competition is to help to develop an algorithm to predict a compound's MoA given its cellular signature, helping scientists advance the drug discovery process. Our execution plan is to apply concepts and algorithms learned in STAT441 and apply multi-label classification. Through the process, our team will learn biological knowledge necessary to complete and enhance our classification thought-process. https://www.kaggle.com/c/lish-moa<br />
<br />
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<br />
'''Project # 15 Group Members:'''<br />
<br />
Li, Evan<br />
<br />
Abuaisha, Karam<br />
<br />
Vadivelu, Nicholas<br />
<br />
Pu, Jason<br />
<br />
'''Title:''' Predict Students Answering Ability Kaggle Competition<br />
<br />
'''Description:'''<br />
<br />
https://www.kaggle.com/c/riiid-test-answer-prediction<br />
We plan on tackling this Kaggle competition that revolves around classifying whether students are able to answer their next questions correctly. The data provided consists of the student’s historic performance, the performance of other students on the same question, metadata about the question itself, and more. The theme of the competition is to tailor education to a student’s ability as an AI tutor.<br />
<br />
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<br />
'''Project # 16 Group members:'''<br />
<br />
Hall, Matthew<br />
<br />
Chalaturnyk, Johnathan<br />
<br />
'''Title:''' Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS<br />
<br />
'''Description:'''<br />
<br />
Predictive emission monitoring systems (PEMS) are used in conjunction with measurement instruments to predict the amount of emissions exuded from Gas turbine engines. The implementation of this system is reliant on the availability of proper measurements and ecological data points. We will attempt to adjust the novel PEMS implementation from this paper in the hopes of improving the prediction of CO and NOx emission levels from the turbines. Using data points collected over the previous five years, we'll use a number of machine learning algorithms to discuss possible future research areas. Finally, we will compare our methods against the benchmark presented in this paper in order to measure the effectiveness of our problem solutions.<br />
<br />
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<br />
'''Project # 17 Group members:'''<br />
<br />
Yang, Junyi<br />
<br />
Wang, Jill Yu Chieh<br />
<br />
Wu, Yu Min<br />
<br />
Li, Calvin<br />
<br />
'''Title:''' Humpback Whale Identification<br />
<br />
'''Description:'''<br />
<br />
Our team will participate in the Kaggle challenge, Humpback Whale Identification. The main objective is to build a multi-class classification model to identify whales' class base on their tail. There are a total of over 3000 classes and 25361 training images. The challenge is that for each class, there are only on average 8 training data. <br />
<br />
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'''Project # 18 Group members:''' <br />
<br />
Lian, Jinjiang <br />
<br />
Zhu, Yisheng <br />
<br />
Huang, Mingzhe <br />
<br />
Hou, Jiawen <br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction <br />
<br />
'''Description:''' <br />
<br />
The final project of our team is the Kaggle ongoing competition -- Mechanism of Action(MoA) Prediction. The goal is to improve the MoA prediction algorithm to assist and advance drug development. MoA algorithm helps scientists approach more targeted medicine molecules based on the biological mechanism of disease. This would strongly shorten the medicine development cycle. Here, MoA here is to apply different drugs to human cells to analyze the corresponding reaction and the dataset provides simultaneous measurement of 100 types of human cells and 5000 drugs. <br />
<br />
To tackle this competition, after data cleaning and feature engineering, we are going to try a selection of ML algorithms such as logistic regression, tree-based method, SVM, etc and find the optimized one that can best complete the tasks. Depending on how we perform, we might utilize other technics such as model ensembling or stacking.<br />
<br />
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'''Project # 19 Group members:''' <br />
<br />
Fagan, Daniel <br />
<br />
Brooke, Cooper <br />
<br />
Perelman, Maya <br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction (https://www.kaggle.com/c/lish-moa/overview/description)<br />
<br />
'''Description:''' <br />
<br />
For our final project, we will be competing in the Mechanisms of Action (MoA) Prediction Research Challenge on Kaggle. MoA refers to the description of the biological activity of a given molecule and scientists have specific interest in the MoA of molecules as it pertains to the advancement of drugs. This is because under new frameworks, scientists are looking to develop molecules that can modulate protein targets associated with given diseases. Our task will be to analyze a dataset containing human cellular responses to more than 5, 000 drugs and to classify these responses with one or more MoA.<br />
<br />
For this competition, we plan to use various classification algorithms taught in STAT 441 followed by model validation techniques to ultimately select the most accurate model based on the logarithmic loss function which was specified by Kaggle.<br />
<br />
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'''Project # 20 Group members:''' <br />
Cheng, Leyan<br />
<br />
Dai, Mingyan<br />
<br />
Jiang, Daniel <br />
<br />
Huang, Jerry<br />
<br />
'''Title:''' Neural Speed Reading via Skim-RNN<br />
<br />
'''Description:'''<br />
<br />
We will be attempting to reproduce the results presented in the paper 'Neural Speed Reading via Skim-RNN' by Min et al. (2018).<br />
<br />
We plan on using the techniques discussed in the paper to complete one of the tasks, namely sentiment analysis on IMDB reviews.<br />
<br />
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'''Project # 21 Group members:''' <br />
<br />
Carson, Emilee<br />
<br />
Ellmen, Isaac<br />
<br />
Mohammadrezaei, Dorsa<br />
<br />
Budaraju, Sai Arvind<br />
<br />
<br />
'''Title:''' Classifying SARS-CoV-2 region of origin based on DNA/RNA sequence<br />
<br />
'''Description:'''<br />
<br />
Determining the location of origin for a viral sequence is an important tool for epidemiological tracking. Knowing where a virus comes from allows epidemiologists to track how a virus is spreading. There are significant efforts to track the spread of SARS-CoV-2. As an RNA virus, SARS-CoV-2 mutates frequently. Most of these mutations carry negligible changes to the function of the virus but act as “barcodes” for specific strains. As the virus spreads in a region, it picks up mutations which allow researchers to identify which sequences correspond to which regions.<br />
<br />
The standard method for classifying viruses based on location is to:<br />
<br />
- Perform a multiple sequence alignment (MSA)<br />
<br />
- Build a phylogenetic tree of the MSA<br />
<br />
- Empirically determine which regions have which sections of the tree<br />
<br />
Phylogenetic trees are an excellent tool for tracking evolutionary changes over time but we wonder if there are better methods for classifying the region of origin for a virus using machine learning techniques.<br />
<br />
Our plan is to perform PCA on the MSA which is available through GISAID. We will determine an appropriate encoding for sequence alignments to vectors and map the aligned sequences onto a much lower dimensional space. We will then use LDA or QDA to classify points based on region (continent). We will also examine if the same technique works well for classifying sequences based on state of origin for samples from the United States. We may try other classification techniques such as logistic regression or neural nets. Finally, we know that projecting data to a small number of principal components and then projecting back to the original space can reduce noise in certain datasets. In the case of mutations, this might correspond to removing insignificant mutations. It is possible that there are certain mutations which induce functional changes in the virus which would be of greater medical interest. Our hope is that we could detect these using PCA.<br />
<br />
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'''Project # 22 Group members:''' <br />
<br />
Chang, Luwen<br />
<br />
Yu, Qingyang<br />
<br />
Kong, Tao <br />
<br />
Sun, Tianrong<br />
<br />
'''Title:''' Riiid! Answer Correctness Prediction<br />
<br />
'''Description:'''<br />
<br />
For the final project, we chose the featured Kaggle Competition named Riiid! Answer Correctness Prediction. The purpose of this challenge is to build a machine learning model to predict the students' interaction performance. (https://www.kaggle.com/c/riiid-test-answer-prediction)<br />
<br />
We plan to use classification and regression techniques learned in this course to build the model and use area under ROC curve to evaluate our model.<br />
<br />
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'''Project # 23 Group members:''' <br />
<br />
Han, Jihoon<br />
<br />
Vera De Casey<br />
<br />
Jawad Solaiman<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles<br />
<br />
'''Description:'''<br />
<br />
We are planning to compete in the Lyft Motion Prediction for Autonomous Vehicles Challenge on Kaggle. Our goal is to build a motion prediction model for the self-driving car by using our machine learning knowledge as well as utilizing the training and testing data sets. The motion prediction model will predict the motion of traffic agents around the car, such as cars, cyclists, and pedestrians. We are not sure if we have to classify the agents into three categories (cars, cyclists, pedestrians) ourselves. If so, we will initially start by using the single-shot detector algorithm and improve through it.<br />
<br />
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'''Project # 24 Group members:''' <br />
<br />
Guanting Pan<br />
<br />
Haocheng Chang <br />
<br />
Zaiwei Zhang<br />
<br />
'''Title:''' Reproducing result in Accelerated Stochastic Power Iteration<br />
<br />
'''Description:'''<br />
<br />
As our final project, we will reproduce the stochastic PCA algorithm designed by De Sa, He, Mitliagkas, Ré, and Xu to accelerate the iteration complexity for power iteration. By doing so, we are aiming to achieve a final rate of 𝒪(1/sqrt(Δ)) for our reproduction result. We are also hoping to explore and discuss the potentiality for applying such an acceleration method to other non-convex optimization problems, as mentioned in the original paper if there is additional time to do so. Link to the paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6557638/pdf/nihms-993807.pdf<br />
<br />
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'''Project # 25 Group members:''' <br />
<br />
Haoran Dong<br />
<br />
Mushi Wang<br />
<br />
Siyuan Qiu<br />
<br />
Yan Yu<br />
<br />
'''Title:''' Lyft Motion Prediction for Autonomous Vehicles<br />
<br />
'''Description:'''<br />
<br />
We want to be involved in the Kaggle Challenge "Lyft Motion Prediction for Autonomous Vehicles". The goal is to build a motion prediction model for the self-driving car by machine learning with the datasets they provided.<br />
<br />
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'''Project # 26 Group members:''' <br />
<br />
Sangeeth Kalaichanthiran<br />
<br />
Evan Peters<br />
<br />
Cynthia Mou<br />
<br />
Yuxin Wang<br />
<br />
'''Title:''' Mechanisms of Action (MoA) Prediction<br />
<br />
'''Description:'''<br />
<br />
Our team chose the "Mechanisms of Action (MoA) Prediction" challenge on Kaggle. Mechanisms of Action, MOA for short, describes the biological response of human cells to a particular molecule (the drug). The goal is to develop an algorithm that can predict the biological response of a drug based on its similarities to other known drugs. <br />
<br />
Our team hopes to develop a superior algorithm by using our knowledge of supervised learning methods.<br />
<br />
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'''Project # 27 Group members:''' <br />
<br />
Delaney Smith<br />
<br />
Mohammad Assem Mahmoud<br />
<br />
'''Title:''' Replicating "Electrocardiogram heartbeat classification based on a deep convolutional<br />
neural network and focal loss"<br />
<br />
'''Description:'''<br />
<br />
For our project, we intend to replicate and hopefully, extend the work of Romdhane et al.’s 2020 paper “Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss”. In this paper, the authors develop a deep convoluted neural network that exploits a novel loss function, focal loss, to classify heartbeats into five arrhythmia categories (N, S, V, Q and F) based on the AAMI standard. The network was trained and tested against two ECG datasets, MIT-BIH and INCART, and returned a 98.41% overall accuracy, a 98.38% overall F1-score, a 98.37% overall prevision and a 98.41% overall recall, which we intend to replicate. <br />
Interestingly, focal loss was implemented to prevent bias towards larger classes (normal heart beats) without needing to augment the smaller class data (diseased heart beats), however the authors did not outline which method actually performs better. Therefore, we hope to extend their work by answering this question in this project.<br />
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'''Project # 28 Group members:''' <br />
<br />
Fang Yuqin<br />
<br />
Fu Rao<br />
<br />
Li Siqi<br />
<br />
Zhou Zeping<br />
<br />
'''Title:''' The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network<br />
<br />
'''Description:'''<br />
Our group aims to dig more on single hidden layer neural network based on what we have learned from class. We'll focus on data that follows the Gaussian distribution and weights such that we can provide some expression in terms of the spectrum in the limit of infinite width. We believe that we can improve the efficiency of first-order optimization problems by applying spectrun. <br />
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'''Project # 29 Group members:''' <br />
<br />
Rui Gong<br />
<br />
Xuetong Wang<br />
<br />
Xinqi Ling<br />
<br />
Di Ma<br />
<br />
'''Title:''' Riiid! Answer Correctness Prediction<br />
<br />
'''Description:'''<br />
<br />
We will take the "Riiid! Answer Correctness Prediction" Kaggle competition. We will predict students' performances on a particular question based on their historic performance. The performance of other students on this question and the information about the question itself (like its difficulty, length, etc). https://www.kaggle.com/c/riiid-test-answer-prediction/overview<br />
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'''Project # 30 Group members:''' <br />
<br />
Jiabao Dong<br />
<br />
Jiaxiang Liu<br />
<br />
Siyuan Xia<br />
<br />
Yipeng Du<br />
<br />
'''Title:''' Riiid! Answer Correctness Prediction<br />
<br />
'''Description:'''<br />
We will be participating in the "Riiid! Answer Correctness Prediction" competition on Kaggle. This competition aims to predict students' performance by applying machine learning techniques. This will involve training models using data about students' past performance, interactions, as well as information about the questions to which the students are being tested on. (https://www.kaggle.com/c/riiid-test-answer-prediction/overview)<br />
<br />
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<br />
'''Project # 31 Group members:''' <br />
<br />
Tompkins, Grace<br />
<br />
Krikella, Tatiana<br />
<br />
'''Title:''' A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting (2018) <br />
'''Description:'''<br />
We will be reproducing the results of "A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting" by Cannas and Arpino (2018) and applying the results to a new dataset, Right Heart Catheterization (RHC) which includes data from the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT), for comparison. This paper uses simulated data and several machine learning algorithms to estimate causal effects in observational studies. The machine learning methods used include CART, Bagging, Boosting, Random Forest, Neural Networks, and Naive Bayes. There are also several variations of measures of covariate balancing used in the study. The importance of tuning the machine learning algorithms' hyperparameters is also investigated with respect to propensity score estimation. <br />
<br />
<br />
We will use R for analysis.<br />
<br />
Link to paper: [http://papers.nips.cc/paper/8520-adapting-neural-networks-for-the-estimation-of-treatment-effects]<br />
<br />
------------------------------------------------------------------------<br />
<br />
'''Project # 32 Group members:''' <br />
<br />
Taohao Wang<br />
Zeren Shen<br />
Zihao Guo<br />
Rui Chen<br />
<br />
'''Title:''' Google Landmark Recognition 2020<br />
<br />
'''Description:'''<br />
Our team decided to give a try for "Google Landmark Recognition 2020" (kaggle) competition,<br />
in which the competitors are asked to build a model to detect any existing landmarks within provided test images.<br />
This competition is challenging in its own way: it has more than 81K classes within its data, where traditional CNN would very<br />
likely to fail(too many parameters to train, especially when taking convolutional layers into account). We will like to implement several <br />
algorithms/frameworks which can utilize a large amount of data with noisy labels, apply them to the provided dataset, and compare their performance(training time, <br />
number of parameters trained, multiple metrics for accuracy/loss evaluation... etc) for our report.<br />
<br />
------------------------------------------------------------------------<br />
<br />
'''Project # 33 Group members:''' <br />
<br />
Hansa Halim<br />
<br />
Sanjana Rajendra Naik<br />
<br />
Samka Marfua<br />
<br />
Shawrupa Proshasty<br />
<br />
'''Title:''' Jane Street Market Prediction Kaggle Competition<br />
<br />
'''Description:'''<br />
Our team will participate in the Jane Street Market Prediction Competition on Kaggle. We will create a model that involves time series to give a prediction to either execute a trade (1) or not (0) on real-time market prices during live trading hours. The model we create will be submitted through an API and will be tested and scored by Kaggle using real-time market data so that means we cannot submit predictions on past market data and that our model is evaluated on future data. <br />
<br />
Link to Kaggle Competition: [https://www.kaggle.com/c/jane-street-market-prediction/ Jane Street Market Prediction]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F20-STAT_441/841_CM_763-Proposal&diff=49928F20-STAT 441/841 CM 763-Proposal2021-09-06T02:54:25Z<p>Aghodsib: Aghodsib moved page F20-STAT 441/841 CM 763-Proposal to F21-STAT 441/841 CM 763-Proposal</p>
<hr />
<div>#REDIRECT [[F21-STAT 441/841 CM 763-Proposal]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=main_Page&diff=49926main Page2021-09-06T02:53:49Z<p>Aghodsib: </p>
<hr />
<div><br />
'''NOTE: Wiki has been migrated from wikicoursenote.com to wiki.math.uwaterloo.ca/statwiki'''<br />
<br />
== [[stat940F21 | Deep Learning (STAT 940- Fall 2021) ]] ==<br />
<br />
== [[stat441F21 | Statistical Learning - Classification (STAT 441/841 CM 763- Fall 2021) ]] ==<br />
<br />
== <br />
'''Archive <br />
''' ==<br />
<br />
== [[stat946F18 | Deep Learning (STAT 946- Fall 2018) ]] ==<br />
== [[stat441F18 | Statistical Learning - Classification (STAT 441/841 CM 763- Fall 2018) ]] ==<br />
<br />
== [[stat946w18 | Deep Learning (STAT 946- Winter 2018) ]] ==<br />
== [[stat441w18 | Statistical Learning - Classification (STAT 441/841 CM 763- Winter 2018) ]] ==<br />
<br />
<br />
== [[stat946f17 | Deep Learning (STAT 946- Fall 2017) ]] ==<br />
<br />
<br />
== [[stat946f15 | Deep Learning (STAT 946- Fall 2015) ]] ==<br />
<br />
== [[stat841f14 | Data Visualization (Stat 442 / 842, CM 762 - Fall 2014) ]] ==<br />
<br />
== [[stat340s13 | Computer Simulation of Complex Systems (Stat 340 - Spring 2013) ]] ==<br />
<br />
== [[stat946s13 | Dimensionality Reduction and Metric Learning (Stat 946 - Spring 2013) ]] ==<br />
== [[stat841f11|Classification (Stat441/841 & CM 463/763-Fall 2011)]] ==<br />
== [[stat946f11|Probabilistic Graphical Models (Stat946-Fall 2011)]] ==<br />
<br />
== [[stat341f11 |Computational Statistics and Data Analysis (Stat 341 & CM 361- Fall 2011) ]] ==<br />
<br />
<br />
<br />
== [[stat946f11pool|Probabilistic Graphical Models (Stat946-Fall 2011) -- Material Pool]] ==<br />
<br />
==Go to [[stat841f10|Stat441/841 & CM 463/763-Fall 2010]] ==<br />
<br />
==Go to [[stat946-Fall 2010]] ==<br />
<br />
----<br />
<br />
==Go to [[stat841|Stat441/841 & CM 463-Fall 2009]] ==<br />
==Go to [[stat946f10|stat946-Spring 2009]] ==<br />
==Go to [[stat341|Stat341 & CM 361]] ==<br />
==Go to [[stat841f11|Stat441/841 & CM 463/763-Fall 2011]] ==<br />
<br />
== HowTo Use Wiki==<br />
<br />
You can take a look to [http://meta.wikimedia.org/wiki/Help:Editing Simple Editing Howto] to learn quickly how you should edit a wiki.<br />
<br />
For writing formulae in wikicoursenote, please take a look at [http://meta.wikimedia.org/wiki/Help:Displaying_a_formula Help:Displaying a formula]. It will definitely help you.<br />
<br />
==A solution to a common problem <span style="color:#ff0000"> (New)</span>==<br />
You may have faced the situation when the math formulas in the body of wikinotes appears extraordinary small (compared to usual font for math formulas). Sometimes this small font helps and sometimes it hurts! One solution to correct this is to simply insert a \, at the ''beginning'' of the formula. This will solve the problem without having any effect on the rest of the formula. For example you should write <mth>\,p_{x,y}</math> instead of <mth>p_{x,y}</math>, to see <math>\,\!p_{x,y}</math> instead of <math>p_{x,y}</math>.<br />
<br />
== Examples ==<br />
<br />
[[Image:Carl gustav jung on TIme's cover.jpg|thumb|100px|right|Carl Gustav Jung]]<br />
<br />
According to scientists, the Sun is pretty big.<ref>E. Miller, The Sun, (New York: Academic Press, 2005), 23-5.</ref><br />
The Moon, however, is not so big.<ref>R. Smith, "Size of the Moon", Scientific American, 46 (April 1978): 44-6.</ref><br />
<br />
<math>\sqrt{x^2+2x+1}=|x+1| - \left(\left(\frac{2x^2}{x}\right)^2\right)^2</math><br />
<br />
Summary<br />
During the lecture on May 9th, we have introduced the concepts of pseudo-random variables. We have used the example of “mod” to clarify the basic idea of generating random variable of uniform (0,1). Also, we have used the example of convertible cdf to show how to generate random variables from uniform(0,1). For each of the example in class, the instructor has used Matlab to show how to reach the desired results in Matlab.<br />
<br />
Multiplicative Congruential Algorithm<br />
We use the operator “mod”<br />
e.g. (10 mod 3) = 1<br />
<br />
if using the recursive form,<br />
(a*x+b mod m) = y<br />
Let a=2, b=1, m=3<br />
<br />
If x=10<br />
(2*10+1 mod 3) =0<br />
(2*0+1 mod 3) = 1<br />
(2*1+1 mod 3) = 0<br />
<br />
Example <br />
a=13 b=0 m=31<br />
The first 30 numbers in the sequence are a permutation of integers from 1 to 30 and then the sequence repeats itself.<br />
Values are between 0 and m-1. If the values are normalized by dividing by m-1, then the results is numbers uniformly distributed in the interval [0,1].<br />
There is only a finite number of values—30 in this case.<br />
<br />
Question: How to generate exp (lambda) from uniform [0,1]?<br />
<br />
Inverse Transform Method<br />
<br />
Theorem<br />
Take u~U(0,1), let x=F<sup>-1</sup>(u)<br />
Then x has distribution function F( ), where F(x)= Pr(X<=x), F<sup>-1</sup>( ) denotes the inverse function of F( ).<br />
<br />
Proof<br />
F(x) = Pr(X<=x)<br />
=Pr (F<sup>-1</sup>(u)<=x)<br />
=Pr(F(F<sup>-1</sup>(u))<=F(x))<br />
=Pr(u<=F(x))<br />
=F(x) (since U~U(0,1))<br />
Example 1<br />
Let f(x)=a*exp^(-a*x)<br />
F(x)=1-exp^(-a*x)<br />
u=1-exp^(-a*x)<br />
x= -1/a*ln(1-u)<br />
F<sup>-1</sup>(x)= -1/a*ln(1-u)<br />
<br />
Therefore, the algorithm is:<br />
1. Draw u~U(0,1)<br />
2. Let x= -1/a*ln(1-u)<br />
<br />
Additional Example:<br />
Write an algorithm to generate a random variable from F(x)=x^12, 0<x<1<br />
Solution:<br />
1. Generate u~U(0,1)<br />
2. u=x^12<br />
x=u^(1/12)<br />
3. output x<br />
we need to show that [[Pi]] si the stationary distribution of this Markov Chain,<br />
[pi]=[pi]P<br />
detailed balance<br />
Remark 1;<br />
A common choice for q(y|x) is a normal distribution centered at X with standard deviation b q(y|x)= N (x, b^2) in this case q(y|x) is symmetric.</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=49922stat441F212021-09-06T02:53:23Z<p>Aghodsib: Aghodsib moved page stat441F20 to stat441F21</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique of the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
[https://www.youtube.com/watch?v=TVLpSFYgF0c&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Influenza_Forecasting_Framework_based_on_Gaussian_Processes Summary]|| [https://www.youtube.com/watch?v=HZG9RAHhpXc&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Gtompkin Summary] || [https://learn.uwaterloo.ca/d2l/ext/rp/577051/lti/framedlaunch/6ec1ebea-5547-46a2-9e4f-e3dc9d79fd54]<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Streaming_Bayesian_Inference_for_Crowdsourced_Classification Summary] || [https://www.youtube.com/watch?v=UgVRzi9Ubws]<br />
|-<br />
|Week of Nov 16 || Matthew Hall, Johnathan Chalaturnyk || 5|| Neural Ordinary Differential Equations || [https://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_ODEs Summary]||<br />
|-<br />
|Week of Nov 16 || Luwen Chang, Qingyang Yu, Tao Kong, Tianrong Sun || 6|| Adversarial Attacks on Copyright Detection Systems || Paper [https://proceedings.icml.cc/static/paper_files/icml/2020/1894-Paper.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Attacks_on_Copyright_Detection_Systems Summary] || [https://www.youtube.com/watch?v=bQI9S6bCo8o]<br />
|-<br />
|Week of Nov 16 || Casey De Vera, Solaiman Jawad || 7|| IPBoost – Non-Convex Boosting via Integer Programming || [https://arxiv.org/pdf/2002.04679.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=IPBoost Summary] || [https://www.youtube.com/watch?v=4DhJDGC4pyI&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Yuxin Wang, Evan Peters, Yifan Mou, Sangeeth Kalaichanthiran || 8|| What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games || [https://arxiv.org/pdf/1805.02777.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=what_game_are_we_playing Summary] || [https://www.youtube.com/watch?v=9qJoVxo3hnI&feature=youtu.be]<br />
|-<br />
|Week of Nov 16 || Yuchuan Wu || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || Zhou Zeping, Siqi Li, Yuqin Fang, Fu Rao || 10|| A survey of neural network-based cancer prediction models from microarray data || [https://www.sciencedirect.com/science/article/pii/S0933365717305067 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Y93fang Summary] || [https://youtu.be/B8pPUU8ypO0]<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:J46hou Summary] || [https://www.youtube.com/watch?v=tvCEvvy54X8&ab_channel=JJLian]<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Networks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Cvmustat Summary] || [https://www.youtube.com/watch?v=or5RTxDnZDo]<br />
|-<br />
|Week of Nov 23 || Taohao Wang, Zeren Shen, Zihao Guo, Rui Chen || 13|| Large Scale Landmark Recognition via Deep Metric Learning || [https://arxiv.org/pdf/1908.10192.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:T358wang Summary] || [https://www.youtube.com/watch?v=K9NypDNPLJo Video]<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Task_Understanding_from_Confusing_Multi-task_Data Summary] || [https://youtu.be/i_5PQdfuH-Y]<br />
|-<br />
|Week of Nov 23 || Rui Gong, Xuetong Wang, Xinqi Ling, Di Ma || 15|| Semantic Relation Classification via Convolution Neural Network|| [https://www.aclweb.org/anthology/S18-1127.pdf paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Semantic_Relation_Classification——via_Convolution_Neural_Network Summary]|| [https://www.youtube.com/watch?v=m9o3NuMUKkU&ab_channel=DiMa video]<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| Graph Structure of Neural Networks || [https://proceedings.icml.cc/paper/2020/file/757b505cfd34c64c85ca5b5690ee5293-Paper.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Graph_Structure_of_Neural_Networks Summary] || [https://youtu.be/x9eUgEwntcs Video]<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Superhuman AI for multiplayer poker || [https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Superhuman_AI_for_Multiplayer_Poker Summary]|| [https://www.youtube.com/watch?v=kazqcOwbtTI Video]<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Point-of-Interest_Recommendation:_Exploiting_Self-Attentive_Autoencoders_with_Neighbor-Aware_Influence Summary] || [https://www.youtube.com/watch?v=aAwjaos_Hus Video]<br />
|-<br />
|Week of Nov 23 || Jerry Huang, Daniel Jiang, Minyan Dai || 19|| Neural Speed Reading Via Skim-RNN ||[https://arxiv.org/pdf/1711.02085.pdf?fbclid=IwAR3EeFsKM_b5p9Ox7X9mH-1oI3U3oOKPBy3xUOBN0XvJa7QW2ZeJJ9ypQVo Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Neural_Speed_Reading_via_Skim-RNN Summary]|| [https://youtu.be/vOENmt9jgVE Video]<br />
|-<br />
|Week of Nov 23 ||Ruixian Chin, Yan Kai Tan, Jason Ong, Wen Cheen Chiew || 20|| DivideMix: Learning with Noisy Labels as Semi-supervised Learning || [https://openreview.net/pdf?id=HJgExaVtwr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Yktan Summary]|| [https://www.youtube.com/watch?v=48xYZXifjS0&ab_channel=SeakraChin]<br />
|-<br />
|Week of Nov 30 || Banno Dion, Battista Joseph, Kahn Solomon || 21|| Music Recommender System Based on Genre using Convolutional Recurrent Neural Networks || [https://www.sciencedirect.com/science/article/pii/S1877050919310646] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Music_Recommender_System_Based_using_CRNN#Evaluation_of_Music_Recommendation_System: Summary] || [https://youtu.be/eGUV3zwLwqQ]<br />
|-<br />
|Week of Nov 30 || Isaac Ellmen, Dorsa Mohammadrezaei, Emilee Carson || 22|| A universal SNP and small-indel variant caller using deep neural networks||[https://www.nature.com/articles/nbt.4235.epdf?author_access_token=q4ZmzqvvcGBqTuKyKgYrQ9RgN0jAjWel9jnR3ZoTv0NuM3saQzpZk8yexjfPUhdFj4zyaA4Yvq0LWBoCYQ4B9vqPuv8e2HHy4vShDgEs8YxI_hLs9ov6Y1f_4fyS7kGZ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=A_universal_SNP_and_small-indel_variant_caller_using_deep_neural_networks Summary] ||<br />
|-<br />
|Week of Nov 30 || Daniel Fagan, Cooper Brooke, Maya Perelman || 23|| Efficient kNN Classification With Different Number of Nearest Neighbors || [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898482 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Dfagan Summary]|| [https://youtu.be/_STVyvm_Kao]<br />
|-<br />
|Week of Nov 30 || Karam Abuaisha, Evan Li, Jason Pu, Nicholas Vadivelu || 24|| Being Bayesian about Categorical Probability || [https://proceedings.icml.cc/static/paper_files/icml/2020/3560-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Being_Bayesian_about_Categorical_Probability Summary] || [https://drive.google.com/file/d/1I0uYF2xEMuNVtaEhPb_vZ6bxSKMi0gUh/view?usp=sharing]<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Loss_Function_Search_for_Face_Recognition] || [https://youtu.be/i3dXnK9HGSQ]<br />
|-<br />
|Week of Nov 30 ||Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee || 26|| Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms || [https://arxiv.org/pdf/1912.07618.pdf?fbclid=IwAR0RwATSn4CiT3qD9LuywYAbJVw8YB3nbex8Kl19OCExIa4jzWaUut3oVB0 Paper] || Summary [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&fbclid=IwAR1Tad2DAM7LT6NXXuSYDZtHHBvN0mjZtDdCOiUFFq_XwVcQxG3hU-3XcaE] || [https://www.youtube.com/watch?v=kiYbAmd_3IA]<br />
|-<br />
|Week of Nov 30 || Stan Lee, Seokho Lim, Kyle Jung, Dae Hyun Kim || 27|| Improving neural networks by preventing co-adaption of feature detectors || [https://arxiv.org/pdf/1207.0580.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Improving_neural_networks_by_preventing_co-adaption_of_feature_detectors Summary] || [https://youtu.be/SV5UOM3QwiA Video]<br />
|-<br />
|Week of Nov 30 || Yawen Wang, Danmeng Cui, ZiJie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid || 28|| A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques || [https://arxiv.org/pdf/1707.02919.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Describtion_of_Text_Mining Summary] || [https://youtu.be/to1fj0GyTAg]<br />
|-<br />
|Week of Nov 30 || Qing Guo, XueGuang Ma, James Ni, Yuanxin Wang || 29|| Mask R-CNN || [https://arxiv.org/pdf/1703.06870.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Mask_RCNN Summary] || [https://youtu.be/NgcSMXNDNuU]<br />
|-<br />
|Week of Nov 30 || Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_Papers_Classification_System Summary] || [https://youtu.be/Ug-5H4B5xkQ]<br />
|-<br />
|Week of Nov 30 || Daniel Zhang, Jacky Yao, Scholar Sun, Russell Parco, Ian Cheung || 31 || Speech2Face: Learning the Face Behind a Voice || [https://arxiv.org/pdf/1905.09773.pdf?utm_source=thenewstack&utm_medium=website&utm_campaign=platform Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Speech2Face:_Learning_the_Face_Behind_a_Voice Summary] || [https://youtu.be/lNQAbMxOj4w]<br />
|-<br />
|Week of Nov 30 || Siyuan Xia, Jiaxiang Liu, Jiabao Dong, Yipeng Du || 32 || Evaluating Machine Accuracy on ImageNet || [https://proceedings.icml.cc/static/paper_files/icml/2020/6173-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Evaluating_Machine_Accuracy_on_ImageNet Summary] || [https://youtu.be/jj4S3VGzQz4 Video]<br />
|-<br />
|Week of Nov 30 || Mushi Wang, Siyuan Qiu, Yan Yu || 33 || Surround Vehicle Motion Prediction Using LSTM-RNN for Motion Planning of Autonomous Vehicles at Multi-Lane Turn Intersections || [https://ieeexplore.ieee.org/abstract/document/8957421 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Surround_Vehicle_Motion_Prediction Summary] || [https://youtu.be/cqyn3aO_5tc Video 33]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:stat441F21&diff=49924Talk:stat441F212021-09-06T02:53:23Z<p>Aghodsib: Aghodsib moved page Talk:stat441F20 to Talk:stat441F21</p>
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<div></div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F20&diff=49923stat441F202021-09-06T02:53:23Z<p>Aghodsib: Aghodsib moved page stat441F20 to stat441F21</p>
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<div>#REDIRECT [[stat441F21]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:stat441F20&diff=49925Talk:stat441F202021-09-06T02:53:23Z<p>Aghodsib: Aghodsib moved page Talk:stat441F20 to Talk:stat441F21</p>
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<div>#REDIRECT [[Talk:stat441F21]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49921stat940F212021-09-06T00:33:13Z<p>Aghodsib: </p>
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<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
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|Week of Nov 8 || Abhinav Chanana (Example) || 1||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://youtu.be/epBzlXHFNlY Presentation ]<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49920stat940F212021-09-06T00:31:51Z<p>Aghodsib: </p>
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<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
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=Paper presentation=<br />
{| class="wikitable"<br />
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|Week of Nov 8 || Abhinav Chanana (Example) || 1||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://youtu.be/epBzlXHFNlY Presentation ]<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49919stat940F212021-09-06T00:26:11Z<p>Aghodsib: </p>
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<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
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=Paper presentation=<br />
{| class="wikitable"<br />
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{| border="1" cellpadding="3"<br />
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|Week of Nov 8 || Abhinav Chanana (Example) || 1||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [https://youtu.be/epBzlXHFNlY Presentation ]<br />
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|Week of Nov 8 || || || || || ||<br />
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|Week of Nov 8 || || || || || ||<br />
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|Week of Nov 8 || || || || || ||<br />
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|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49918stat940F212021-09-06T00:24:07Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
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|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
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|Week of Nov 11 || Abhinav Chanana (Example) || 1||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [https://youtu.be/epBzlXHFNlY Presentation ]<br />
|-<br />
|Week of Nov 11 || || || || || ||<br />
|-<br />
|Week of Nov 11 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 11 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 11 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49917stat940F212021-09-06T00:22:15Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
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{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
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<br />
|Week of Nov 11 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 11 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 11 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 11 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 11 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49916stat940F212021-09-06T00:19:43Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/epBzlXHFNlY Presentation ]<br />
|Week of Nov 11 || || 1|| || || || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 11 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 11 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 11 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 11 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 11 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49915stat940F212021-09-06T00:19:01Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 11 || || 1|| || || || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 11 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 11 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 11 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 11 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 11 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49914stat940F212021-09-06T00:16:52Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 11 || || 1|| || || || <br />
|-<br />
|Week of Nov 11 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 11 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 11 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 11 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 11 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49913stat940F212021-09-06T00:15:24Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 11 || Jose Avilez || 1|| Gradientless Descent: High-Dimensional Zeroth-Order Optimisation || [https://openreview.net/pdf?id=Skep6TVYDB] || [[GradientLess Descent]] || [https://uofwaterloo-my.sharepoint.com/:v:/g/personal/jlavilez_uwaterloo_ca/ETNogDRpwJlPjSo5o0EY53UBLC7f0zmR9--a0uz6GYN8zw?e=J8V0f3 GLD Presentation] [[File:GradientLessDescent.pdf|Slides]] <br />
|-<br />
|Week of Nov 2 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 2 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 2 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 2 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 2 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49912stat940F212021-09-06T00:14:41Z<p>Aghodsib: /* Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing] */</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 2 || Jose Avilez || 1|| Gradientless Descent: High-Dimensional Zeroth-Order Optimisation || [https://openreview.net/pdf?id=Skep6TVYDB] || [[GradientLess Descent]] || [https://uofwaterloo-my.sharepoint.com/:v:/g/personal/jlavilez_uwaterloo_ca/ETNogDRpwJlPjSo5o0EY53UBLC7f0zmR9--a0uz6GYN8zw?e=J8V0f3 GLD Presentation] [[File:GradientLessDescent.pdf|Slides]] <br />
|-<br />
|Week of Nov 2 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 2 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 2 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 2 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 2 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
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|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
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|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
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|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
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|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
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|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
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|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
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|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_940-Proposal&diff=49911F21-STAT 940-Proposal2021-09-06T00:14:19Z<p>Aghodsib: Replaced content with "Use this format (Don’t remove Project 0) Project # 0 Group members: Last name, First name Last name, First name Last name, First name Last name, First name Title:..."</p>
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<div>Use this format (Don’t remove Project 0)<br />
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Title: Making a String Telephone<br />
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Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_940-Proposal&diff=49909F21-STAT 940-Proposal2021-09-06T00:13:28Z<p>Aghodsib: Aghodsib moved page F20-STAT 946-Proposal to F21-STAT 940-Proposal</p>
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<div>Use this format (Don’t remove Project 0)<br />
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Project # 0 Group members:<br />
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Last name, First name<br />
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Last name, First name<br />
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Last name, First name<br />
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Last name, First name<br />
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Title: Making a String Telephone<br />
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Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
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Project # 1 Group members:<br />
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McWhannel, Pierre<br />
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Yan, Nicole<br />
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Hussein Salamah, Ahmed <br />
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Title: Dense Retrieval for Conversational Information Seeking <br />
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Description:<br />
One of the recognized problems in Information Retrieval (IR) is the conversational search that attracts much attention in form of Conversational Assistants such as Alexa, Siri and Cortana. The users’ needs are the ultimate goal of conversational search systems, in this context the questions are asked sequentially imposing a multi-turn format as the Conversational Information Seeking (CIS) task. TREC Conversational Assistance Track (CAsT) [3] is a multi-turn conversational search task as it contains a large-scale reusable test collection for sequences of conversational queries. The response of this conversational model is not a list of relevant documents, but it is limited to brief response passages with a length of 1 to 3 sentences in length.<br />
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[[File:Screen Shot 2020-10-09 at 1.33.00 PM.png | 300px | Example Queries in CAsT]]<br />
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In [4], the authors focus on improving open domain question answering by including dense representations for retrieval instead of the traditional methods. They have adopted a simple dual-encoder framework to construct a learnable retriever on large collections. We want to adopt this dense representation for the conversational model in the CAsT task and compare it with the performance of the other approaches in literature. The performance will be indicated by using graded relevance on five point, which are Fails to meet, Slightly meets, Moderately meets, Highly meets, and Fully meets.<br />
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We aim to further improve our system performance by integrating the following techniques:<br />
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• Paragraph-level pre-training tasks: ICT, BFS, and WLP [1]<br />
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• ANCE training: periodically using checkpoints to encode documents, from which the strong negatives close to the relevant document would be used as next training negatives [5]<br />
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In summary, this project is exploratory in nature as we will be trying to use state-of-art Dense Passage Retrieval techniques (based on BERT) [4, 6], in a question answering (QA) problem. Current first-stage-retrieval approaches mainly rely on bag-of-words models. In this project, we hope to explore the feasibility of using state-of-art methods such as BERT. We will first compare how these perform on the TREC CAsT datasets [3] against the results retrieved using BM25. After these first points of comparison we will next explore methods of improving DPR by exploring one or more techniques that are made to improve the performance of DPR. [1, 5].<br />
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References<br />
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[1] Wei-Cheng Chang et al. Pre-training Tasks for Embedding-based Large-scale Retrieval. 2020. arXiv: 2002.03932 [cs.LG].<br />
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[2] Zhuyun Dai and Jamie Callan. Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval. 2019. arXiv: 1910.10687 [cs.IR].<br />
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[3] Jeffrey Dalton, Chenyan Xiong, and Jamie Callan. TREC CAsT 2019: The Conversational Assistance Track Overview. 2020. arXiv: 2003.13624 [cs.IR].<br />
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[4] Vladimir Karpukhin et al. Dense Passage Retrieval for Open-Domain Ques- tion Answering. 2020. arXiv: 2004.04906 [cs.CL].<br />
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[5] Lee Xiong et al. Approximate Nearest Neighbor Negative Contrastive Learn- ing for Dense Text Retrieval. 2020. arXiv: 2007.00808 [cs.IR].<br />
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[6] Jingtao Zhan et al. RepBERT: Contextualized Text Embeddings for First- Stage Retrieval. 2020. arXiv: 2006.15498 [cs.IR].<br />
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Project # 2 Group members:<br />
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Singh, Gursimran<br />
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Sharma, Govind<br />
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Chanana, Abhinav<br />
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Title: Quick Text Description using Headline Generation and Text To Image Conversion<br />
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Description: An automatic tool to generate short description based on long textual data is a useful mechanism to share quick information. Most of the current approaches involve summarizing the text using varied deep learning approaches from Transformers to different RNNs. For this project, instead of building a standard text summarizer, we aim to provide two separate utilities for generating a quick description of the text. First, we plan to develop a model that produces a headline for the long textual data, and second, we are intending to generate an image describing the text. <br />
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Headline Generation - Headline generation is a specific case of text summarization where the output is generally a combination of few words that gives an overall outcome from the text. In most cases, text summarization is an unsupervised learning problem. But, for the headline generation, we have the original headlines available in our training dataset that makes it a supervised learning task. We plan to experiment with different Recurrent Neural Networks like LSTMs and GRUs with varied architectures. For model evaluation, we are considering BERTScore using which we can compare the reference headline with the automatically generated headline from the model. We also aim to explore Attention and Transformer Networks for the text (headline) generation. We will make use of the currently available techniques mentioned in the various research papers but also try to develop our own architecture if the previous methods don't reveal reliable results on our dataset. Therefore, this task would primarily fit under the category of application of deep learning to a particular domain, but could also include some components of new algorithm design.<br />
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Text to Image Conversion - Generation or synthesis of images from a short text description is another very interesting application domain in deep learning. One approach for image generation is based on mapping image pixels to specific features as described by the discriminative feature representation of the text. Recurrent Neural Networks have been successfully used in learning such feature representations of text. This approach is difficult to generalize because the recognition of discriminative features for texts in different domains is not an easy task and it requires domain expertise. Different generative methods have been used including Variational Recurrent Auto-Encoders and its extension in Deep Recurrent Attention Writer (DRAW). We plan to experiment with Generative Adversarial Networks (GAN). Application of GANs on domain-specific datasets has been done but we aim to apply different variants of GANs on the Microsoft COCO dataset which has been used in other architectures. The analysis will be focusing on how well GANs are able to generalize when compared to other alternatives on the given dataset.<br />
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Scope - The above models will be trained independently on different datasets. Therefore, for a particular text, only one of the two functionalities will be available.<br />
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Project # 3 Group members:<br />
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Sikri, Gaurav<br />
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Bhatia, Jaskirat<br />
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Title: Cassava Leaf Disease Classification (Kaggle Competition)<br />
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Description: Cassava is a major food crop harvested by farmers in Africa due to its nature to withstand harsh conditions. But in the last few years, the frequency of viral diseases to leaves has become a major concern for the farmers. Currently-existing methods of disease discovery require farmers to solicit the help of government-funded agricultural experts to visually inspect and diagnose the plants. This is a lot challenging since it is very expensive and labor-intensive. <br />
This task is organized by The Makerere Artificial Intelligence (AI) Lab, which is an AI and Data Science research group based at Makerere University in Uganda. The ask is to predict the type of disease that the leaves have by looking at the image of the leaves. <br />
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Kaggle link:- https://www.kaggle.com/c/cassava-leaf-disease-classification/overview<br />
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Project # 4 Group members:<br />
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Maleki, Danial<br />
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Rasoolijaberi, Maral<br />
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Title: Binary Deep Neural Network for the domain of Pathology<br />
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Description: The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. We want to investigate the possibility of using these types of networks in the domain of histopathology as it has gigapixels images which make the use of them very useful.<br />
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Project # 5 Group members:<br />
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Jain, Abhinav<br />
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Bathla, Gautam<br />
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Title: Zero short learning with AREN and HUSE<br />
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Description: Attention Region Discovery and Adaptive Thresholding module are taken from the idea of “Attentive Region Embedding Network for Zero-shot Learning” (https://openaccess.thecvf.com/content_CVPR_2019/papers/Xie_Attentive_Region_Embedding_Network_for_Zero-Shot_Learning_CVPR_2019_paper.pdf) whereas the idea for projecting image and text embeddings into a shared space was taken by “HUSE: Hierarchical Universal Semantic Embeddings” (https://arxiv.org/pdf/1911.05978.pdf). The motivation is that the attribute embedding can provide some complementary information to the model which can be learned to represent into a shared space and hence a better prediction to the zero-shot learning can be made. Also, the Squeeze and Excitation layer showed some impressive results when applied to the feature extraction part of the model, therefore we thought of re-weighting the channels first before applying the self-attention module so that the model can give even better attention to the image. The paper “Attentive Region Embedding Network for Zero-shot Learning” projected image features directly to the semantic space to make zero-shot predictions but HUSE showed that models can learn better when image and semantic features are projected to a shared space, therefore we wanted to see if the model can make use of this shared space and hence enhance the performance.<br />
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[[File:a227jain-proposal.jpeg | 300px | Architecture diagram]]<br />
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Project # 6 Group members:<br />
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You, Bowen<br />
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Wu, Mohan<br />
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Title: Deep Learning Models in Volatility Forecasting<br />
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Description: Price forecasting has become a very hot topic in the financial industry in recent years. We are however very interested in the volatility of such financial instruments. We propose a new deep learning architecture or model to predict volatility and apply our model to real life datasets of various financial products. We will analyze our results and compare them to more traditional methods.<br />
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Project # 7 Group members:<br />
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Chen, Meixi<br />
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Shen, Wenyu<br />
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Title: Through the Lens of Probability Theory: A Comparison Study of Bayesian Deep Learning Methods<br />
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Description: Deep neural networks have been known as black box models, but they can be made less mysterious when adopting a Bayesian approach. From a Bayesian perspective, one is able to assign uncertainty on the weights instead of having single point estimates, which allows for a better interpretability of deep learning models. However, Bayesian deep learning methods are often intractable due an increase amount of parameters and often times don't have as good performance. In this project, we will study different BDL methods such as Bayesian CNN using variational inference and Laplace approximation, with applications on image classification, and we will try to propose improvements where possible.<br />
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Project # 8 Group members:<br />
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Avilez, Jose<br />
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Title: A functional universal approximation theorem<br />
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Description: In the seminal paper "Approximation by superpositions of a sigmoidal function", Cybenko gave a simple proof using elementary functional analysis that a certain class of functions, called discriminatory functions, serve as valid activation functions for universal neural approximators. The objective of our project is three-fold:<br />
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1) Prove a converse of Cybenko's Universal Approximation Theorem by means of the Stone-Weierstrass theorem<br />
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2) Provide examples and non-examples of Cybenko's discriminatory functions<br />
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3) Construct a neural network for functional data (i.e. data arising in function spaces) and prove a universal approximation theorem for Lp spaces.<br />
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References:<br />
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[1] Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.<br />
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[2] Folland, Gerald B. Real analysis: modern techniques and their applications. Vol. 40. John Wiley & Sons, 1999.<br />
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[3] Ramsay, J. O. (2004). Functional data analysis. Encyclopedia of Statistical Sciences, 4.<br />
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Project # 9 Group members:<br />
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Sikaroudi, Milad<br />
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Ashrafi Fashi, Parsa<br />
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Title: '''Magnification Generalization with Model-Agnostic Semantic Features in Histopathology Images'''<br />
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Many of the embedding methods learn the subspace for only a specific magnification. However, one of the main challenges in histopathology image embedding is the different magnification levels for indexing of a Whole Slide Indexing (WSI) image [1]. It is well-known that significantly different patterns may exist at different magnification levels of a WSI [2]. <br />
It is useful to train an embedding space for discriminating the histopathology patches regardless of their magnifications. That would lead to learning more compact WSI representations. It has been an arduous task because of the significant domain shifts between different magnification levels with noticeably different patterns. The performance of conventional deep neural networks tends to degrade in the presence of a domain shift, such as the gathering of data from different centers. In this study for the first time, we are going to introduce different magnification levels as a domain shift to see if we can generalize to in-common features in different magnification levels by means of a domain generalization technique, known as Model Agnostic Learning of Semantic Features. The hypothesis is that the statistics of retrieval for the model trained using episodic domain generalization will not degrade as much as the baseline when there is a domain shift. <br />
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[1] Sellaro, Tiffany L., et al. "Relationship between magnification and resolution in digital pathology systems." Journal of pathology informatics 4 (2013).<br />
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[2] Zaveri, Manit, et al. "Recognizing Magnification Levels in Microscopic Snapshots." arXiv preprint arXiv:2005.03748 (2020).<br />
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Project # 10 Group members:<br />
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Torabian, Parsa<br />
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Ebrahimi Farsangi, Sina<br />
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Moayyedi, Arash<br />
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Title: Meta-Learning Regularizers for Few-Shot Classification Models<br />
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Our project aims at exploring the effects of self-supervised pre-training on few-shot classification. We draw inspiration from the paper “When Does Self-supervision Improve Few-shot Learning?”[1] where the authors analyse the effects of using the Jigsaw puzzle[2] and rotation tasks as regularizers for training Prototypical Networks[3] and Model-Agnostic Meta-Learning (MAML)[4] networks. <br />
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The introduced paper analyzes the effects of regularizing meta-learning models using self-supervised loss, based on rotation and Jigsaw tasks. We will apply PIRL [5], a contrastive self-supervised approach which recently set records, to the few-shot task. We will also look at other regularization techniques which are successful in classical deep learning regimes such as orthogonality regularization, to see their effects in few-shot learning. We will test our methods against ProtoNets and MAML, as representative of the metric and optimization-based meta-learning approaches respectively. <br />
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References:<br />
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[1] https://arxiv.org/abs/1910.03560<br />
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[2] https://arxiv.org/abs/1603.09246<br />
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[3] https://arxiv.org/abs/1703.05175 <br />
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[4] https://arxiv.org/abs/1703.03400<br />
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[5] https://arxiv.org/abs/1912.01991<br />
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Project # 11 Group Members:<br />
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Shikhar Sakhuja: s2sakhuj@uwaterloo.ca <br />
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Introduction:<br />
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Controller Area Network (CAN bus) is a vehicle bus standard that allows Electronic Control Units (ECU) within an automobile to communicate with each other without the need for a host computer. Modern automobiles might have up to 70 ECUs for various subsystems such as Engine, Transmission, Breaking, etc. The ECUs exchange messages on the CAN bus and allow for a lot of modern vehicle capabilities such as automatic start/stop, electric park brakes, lane detection, collision avoidance, and more. Each message exchanged on the bus is encoded as a 29-bit packet. These 29 bits consist of a combination of Parameter Group Number (PGN), message priority, and the source address of the message. Parameter groups can be, for example, engine temperature which could include coolant temperature, fuel temperature, etc. The PGN itself includes information such as priority, reserved status, data page, and PDU format. Lastly, the source address maps the message to the ECU it originates from. <br />
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Goals:<br />
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(1) This project aims to use messages exchanged on the CAN bus of a Challenger Truck collected by the Embedded Systems Group at the University of Waterloo. The data exists in a temporal format with a new message exchanged periodically. The goals of this project are two folds:<br />
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(2) Predicting the PGN and source address of message N exchanged on the bus, given messages 1 to N-1. We might also explore predicting attributes within the PGN. <br />
Predicting the delay between messages N-1 and N, given the delay between each pair of consecutive messages leading up to message N-1. <br />
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Potential Approach:<br />
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For the first goal, we intend to experiment with RNN models along with Attention modules since they have shown promising results in text generation/prediction. <br />
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The second goal is more of an investigative problem where we intend to use regression techniques powered by Neural Networks to predict delays between messages N-1 and N.<br />
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Project # 12 Group members:<br />
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Hemati, Sobhan <br />
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Meaney, Cameron <br />
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Title: Representation learning of gigapixel histopathology images using PointNet a permutation invariant neural network<br />
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Description:<br />
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In recent years, there has been a significant growth in the amount of information available in digital pathology archives. This data is valuable because of its potential uses in research, education, and pathologic diagnosis. As a result, representation learning of histopathology whole slide images (WSIs) has attracted significant attention and become an active area of research. Unfortunately, scientific progress with these data have been difficult because of challenges inherent to the data itself. These challenges include highly complex textures of different tissue types, color variations caused by different stainings, and most notably, the size of the images which are often larger than 50,000x50,000 pixels. Additionally, these images are multi-resolution meaning that each WSI may contain images from different zoom levels, primarily 5X, 10X, 20X, and 40X. With the advent of deep learning, there is optimism that these challenges can be overcome. The main challenge in this approach is that the sheer size of the images makes it infeasible (or impossible) to obtain a vector representation for a WSI, which is a necessary step in order to leverage deep learning algorithms. In practice, this is often bypassed by considering ‘patches’ of the WSI of smaller sizes, a set of which is meant to represent the full WSI. This approach lead to a set representation for a WSI. However, unlike traditional image or sequence models, deep networks that process and learn permutation invariant representations from sets is still a developing area of research. Recent attempts at this include Multi-instance Learning Schemes, Deep Set, and Set Transformers. A particularly successful attempt in developing a deep neural network for set representation in called PointNet which was developed for classification and segmentation of 3D objects and point clouds. In PointNet, each set is represented using a set of (x,y,z) coordinates, and the network is designed to learn a permutation invariant global representation for each set and then use this representation for classification or segmentation.<br />
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In this project, we attempt to first extend the PointNet network to a convolutional PointNet network such that it uses a set of image patches rather than (x,y,z) coordinates to learn the universal permutation invariant representation. Then, we attempt improve the representational power of PointNet as a permutation invariant neural network. For the first part, the main challenge is that while PointNet has been designed for processing of sets with the same size, in WSIs, the size of the image and therefore number of patches is not fixed. For this reason, we will need to develop an idea which enables CNN-PointNet to process sets with different sizes. One possible solution is to use fake members to standardize the set size and then remove the effect of these fake members in backpropagation using a masking scheme. For the second part, the PointNet network can be improved in many ways. For example, the rotation matrix used is not a real rotation matrix as the orthogonality is incorporated using a regularization term. However, using a projected gradient technique and the existence of a closed form solution for obtaining nearest orthogonal matrix to a given matrix (Orthogonal Procrustes Problem) we can keep the exact orthogonality constraint and obtain a real rotation matrix. This exact orthogonality is geometrically important as, otherwise, this transformation will likely corrupt the neighborhood structure of the points in each set. Furthermore, PointNet uses very simple symmetric function (max pooling) as a set approximator, however there more powerful symmetric functions like statistical moments, power-sum with a trainable parameter, and other set approximators can be used. It would be interesting to see how more complicated symmetric functions can improve the representational power of PointNet to achieve more discriminative permutation invariant representations for each set (in this case WSIs).<br />
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Project # 13 Group Members:<br />
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Syed Saad Naseem ssnaseem@uwaterloo.ca<br />
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Title: Text classification of topics related to COVID-19 on social media using deep learning<br />
The COVID-19 pandemic has become a public health emergency and a critical socioeconomic issue worldwide. It is changing the way we live and do business. Social media is a rich source of data about public opinion on different types of topics including topics about COVID-19. I plan on using Reddit to get a dataset of posts and comments from users related to COVID-19 and since Reddit is divided into communities so the posts and comments are also clustered by the topic of the community, for example, posts from the political subreddit will have posts about politics.<br />
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I plan to make a classifier that will take a given text and will tell what the text of talking about for example it can be talking about politics, studies, relationships, etc. The goals of this project are to:<br />
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• Scrape a dataset from Reddit from different communities<br />
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• Train a deep learning model (CNN or RNN model) to classify a given text into the possible categories<br />
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• Test the model on posts from social talking about COVID-19<br />
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Project # 14 Group members<br />
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Edwards, John<br />
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Title: Click-through Rate Prediction Using Historical User Data<br />
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Click-through Rate (CTR) prediction consists of forecasting a users probability of clicking on a specified target. CTR is used largely by online advertising systems which sell ad space on a cost-per-click pricing model to asses the likenesses of a user clicking on a targeted ad. <br />
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User session logs provides firms with an assortment of individual specific features, a large - number of which are categorical. Additionally, advertisers posses multiple ad candidates each with their own respective features. The challenge of CTR prediction is to design a model which encompass the Interacting effects of these features to produced high quality forecasts and pair users with advertisements with high potential for click conversion. Additionally computational efficiency must balanced with model complexity so that predictions can be done in an online setting throughout the progression of a users session.<br />
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This projects primary objective will be to attempt creating a new Deep Neural Network (DNN) architecture for producing high quality CTR forecasts while also satisfying the aforementioned challenges.<br />
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While many variants of DNN for CTR predictions exists they can differ greatly in application setting. Specifically, the vast majority of models evaluate each user-ad interaction independently. They fail to utlise information contained for each specific users’ historical add impressions. There is only a small subset of models [1,2,4] which have tried to address this by adapting architectures to utilize historical information. This projects focus will be within this application setting exploring new architectures which can better utilise information contained within a users historical behaviour. <br />
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This projects implementation will consist of the following action plan:<br />
Develop a new model architecture inspired by innovations of previous CTR network designs which lacked the ability to adapt their model to utlize a users historical data [4,5].<br />
Use the public benchmark Avito advertising dataset to empirically evaluate the new models performance and compare it against previous state of the art models for this data set. <br />
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References:<br />
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[1] Ouyang, Wentao & Zhang, Xiuwu & Ren, Shukui & Li, Li & Liu, Zhaojie & Du, Yanlong. (2019). Click-Through Rate Prediction with the User Memory Network. <br />
<br />
[2] Ouyang, Wentao & Zhang, Xiuwu & Li, Li & Zou, Heng & Xing, Xin & Liu, Zhaojie & Du, Yanlong. (2019). Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction. 2078-2086. 10.1145/3292500.3330655. <br />
<br />
[3] Ouyang, Wentao & Zhang, Xiuwu & Ren, Shukui & Qi, Chao & Liu, Zhaojie & Du, Yanlong. (2019). Representation Learning-Assisted Click-Through Rate Prediction. 4561-4567. 10.24963/ijcai.2019/634. <br />
<br />
[4] Li, Zeyu, Wei Cheng, Yang Chen, H. Chen and W. Wang. “Interpretable Click-Through Rate Prediction through Hierarchical Attention.” Proceedings of the 13th International Conference on Web Search and Data Mining (2020)<br />
<br />
[5] Zhou, Guorui & Gai, Kun & Zhu, Xiaoqiang & Song, Chenru & Fan, Ying & Zhu, Han & Ma, Xiao & Yan, Yanghui & Jin, Junqi & Li, Han. (2018). Deep Interest Network for Click-Through Rate Prediction. 1059-1068. 10.1145/3219819.3219823.<br />
<br />
<br />
Project #15 Group members<br />
<br />
Donya Hamzeian<br />
Maziar Dadbin<br />
<br />
Title: Mechanisms of Action (MoA) Prediction(Kaggle project)<br />
<br />
<br />
Description: This project is organized by the Laboratory for Innovation Science at Harvard with the goal of advancing drug development through improvements to MoA prediction algorithms. <br />
<br />
What is the Mechanism of Action (MoA) of a drug? And why is it important?<br />
<br />
In the past, scientists derived drugs from natural products or were inspired by traditional remedies. Very common drugs, such as paracetamol, known in the US as acetaminophen, were put into clinical use decades before the biological mechanisms driving their pharmacological activities were understood. Today, with the advent of more powerful technologies, drug discovery has changed from the serendipitous approaches of the past to a more targeted model based on an understanding of the underlying biological mechanism of a disease. In this new framework, scientists seek to identify a protein target associated with a disease and develop a molecule that can modulate that protein target. As a shorthand to describe the biological activity of a given molecule, scientists assign a label referred to as mechanism-of-action or MoA for short.<br />
<br />
Here is a brief explanation of how the MoA of a drug is determined:<br />
The approach used in this project to determine the MoA is by testing the drug on a sample of human cells, including control and treatment groups, with different doses. Then, after a certain period of time, their cellular responses, i.e. gene expressions and cell viability, were measured which are deterministic of the drug's mechanisms of action and comprise the feature. The mechanisms of action data, including 206 columns, are binary targets that in this project we aim to predict. Therefore, the whole project is a multilabel binary classification. The data used for training consists of cellular responses to different combinations of drugs and human cell samples with their MoA's which are the columns to be predicted in the test data.<br />
<br />
<br />
Project # 16 <br />
<br />
Group members:<br />
<br />
Nouha Chatti<br />
<br />
Title: Detecting contradiction and entailment in multilingual text<br />
<br />
Description:<br />
The project addresses a Natural Language Inference (NLI) problem. NLI is a task of Natural Language Processing which recognizes textual contradiction and entailment. This project takes place in the framework of a Kaggle competition. The goal consists in implementing a multilingual model able to accurately classify a hypothesis to the following classes: entailment, contradiction or neutral, based on a given premise. This solution can be very useful in various domain. For instance, it can be used to detect contradictions and conflicting sentences in text in general or to identify defiance and inconsistency in political statements or even to analyze users’ opinions and customers’ reviews posted on social platforms.</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F20-STAT_946-Proposal&diff=49910F20-STAT 946-Proposal2021-09-06T00:13:28Z<p>Aghodsib: Aghodsib moved page F20-STAT 946-Proposal to F21-STAT 940-Proposal</p>
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<div>#REDIRECT [[F21-STAT 940-Proposal]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=main_Page&diff=49908main Page2021-09-06T00:12:47Z<p>Aghodsib: </p>
<hr />
<div><br />
'''NOTE: Wiki has been migrated from wikicoursenote.com to wiki.math.uwaterloo.ca/statwiki'''<br />
<br />
== [[stat940F21 | Deep Learning (STAT 940- Fall 2021) ]] ==<br />
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== [[stat441F20 | Statistical Learning - Classification (STAT 441/841 CM 763- Fall 2020) ]] ==<br />
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== <br />
'''Archive <br />
''' ==<br />
<br />
== [[stat946F18 | Deep Learning (STAT 946- Fall 2018) ]] ==<br />
== [[stat441F18 | Statistical Learning - Classification (STAT 441/841 CM 763- Fall 2018) ]] ==<br />
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== [[stat946w18 | Deep Learning (STAT 946- Winter 2018) ]] ==<br />
== [[stat441w18 | Statistical Learning - Classification (STAT 441/841 CM 763- Winter 2018) ]] ==<br />
<br />
<br />
== [[stat946f17 | Deep Learning (STAT 946- Fall 2017) ]] ==<br />
<br />
<br />
== [[stat946f15 | Deep Learning (STAT 946- Fall 2015) ]] ==<br />
<br />
== [[stat841f14 | Data Visualization (Stat 442 / 842, CM 762 - Fall 2014) ]] ==<br />
<br />
== [[stat340s13 | Computer Simulation of Complex Systems (Stat 340 - Spring 2013) ]] ==<br />
<br />
== [[stat946s13 | Dimensionality Reduction and Metric Learning (Stat 946 - Spring 2013) ]] ==<br />
== [[stat841f11|Classification (Stat441/841 & CM 463/763-Fall 2011)]] ==<br />
== [[stat946f11|Probabilistic Graphical Models (Stat946-Fall 2011)]] ==<br />
<br />
== [[stat341f11 |Computational Statistics and Data Analysis (Stat 341 & CM 361- Fall 2011) ]] ==<br />
<br />
<br />
<br />
== [[stat946f11pool|Probabilistic Graphical Models (Stat946-Fall 2011) -- Material Pool]] ==<br />
<br />
==Go to [[stat841f10|Stat441/841 & CM 463/763-Fall 2010]] ==<br />
<br />
==Go to [[stat946-Fall 2010]] ==<br />
<br />
----<br />
<br />
==Go to [[stat841|Stat441/841 & CM 463-Fall 2009]] ==<br />
==Go to [[stat946f10|stat946-Spring 2009]] ==<br />
==Go to [[stat341|Stat341 & CM 361]] ==<br />
==Go to [[stat841f11|Stat441/841 & CM 463/763-Fall 2011]] ==<br />
<br />
== HowTo Use Wiki==<br />
<br />
You can take a look to [http://meta.wikimedia.org/wiki/Help:Editing Simple Editing Howto] to learn quickly how you should edit a wiki.<br />
<br />
For writing formulae in wikicoursenote, please take a look at [http://meta.wikimedia.org/wiki/Help:Displaying_a_formula Help:Displaying a formula]. It will definitely help you.<br />
<br />
==A solution to a common problem <span style="color:#ff0000"> (New)</span>==<br />
You may have faced the situation when the math formulas in the body of wikinotes appears extraordinary small (compared to usual font for math formulas). Sometimes this small font helps and sometimes it hurts! One solution to correct this is to simply insert a \, at the ''beginning'' of the formula. This will solve the problem without having any effect on the rest of the formula. For example you should write <mth>\,p_{x,y}</math> instead of <mth>p_{x,y}</math>, to see <math>\,\!p_{x,y}</math> instead of <math>p_{x,y}</math>.<br />
<br />
== Examples ==<br />
<br />
[[Image:Carl gustav jung on TIme's cover.jpg|thumb|100px|right|Carl Gustav Jung]]<br />
<br />
According to scientists, the Sun is pretty big.<ref>E. Miller, The Sun, (New York: Academic Press, 2005), 23-5.</ref><br />
The Moon, however, is not so big.<ref>R. Smith, "Size of the Moon", Scientific American, 46 (April 1978): 44-6.</ref><br />
<br />
<math>\sqrt{x^2+2x+1}=|x+1| - \left(\left(\frac{2x^2}{x}\right)^2\right)^2</math><br />
<br />
Summary<br />
During the lecture on May 9th, we have introduced the concepts of pseudo-random variables. We have used the example of “mod” to clarify the basic idea of generating random variable of uniform (0,1). Also, we have used the example of convertible cdf to show how to generate random variables from uniform(0,1). For each of the example in class, the instructor has used Matlab to show how to reach the desired results in Matlab.<br />
<br />
Multiplicative Congruential Algorithm<br />
We use the operator “mod”<br />
e.g. (10 mod 3) = 1<br />
<br />
if using the recursive form,<br />
(a*x+b mod m) = y<br />
Let a=2, b=1, m=3<br />
<br />
If x=10<br />
(2*10+1 mod 3) =0<br />
(2*0+1 mod 3) = 1<br />
(2*1+1 mod 3) = 0<br />
<br />
Example <br />
a=13 b=0 m=31<br />
The first 30 numbers in the sequence are a permutation of integers from 1 to 30 and then the sequence repeats itself.<br />
Values are between 0 and m-1. If the values are normalized by dividing by m-1, then the results is numbers uniformly distributed in the interval [0,1].<br />
There is only a finite number of values—30 in this case.<br />
<br />
Question: How to generate exp (lambda) from uniform [0,1]?<br />
<br />
Inverse Transform Method<br />
<br />
Theorem<br />
Take u~U(0,1), let x=F<sup>-1</sup>(u)<br />
Then x has distribution function F( ), where F(x)= Pr(X<=x), F<sup>-1</sup>( ) denotes the inverse function of F( ).<br />
<br />
Proof<br />
F(x) = Pr(X<=x)<br />
=Pr (F<sup>-1</sup>(u)<=x)<br />
=Pr(F(F<sup>-1</sup>(u))<=F(x))<br />
=Pr(u<=F(x))<br />
=F(x) (since U~U(0,1))<br />
Example 1<br />
Let f(x)=a*exp^(-a*x)<br />
F(x)=1-exp^(-a*x)<br />
u=1-exp^(-a*x)<br />
x= -1/a*ln(1-u)<br />
F<sup>-1</sup>(x)= -1/a*ln(1-u)<br />
<br />
Therefore, the algorithm is:<br />
1. Draw u~U(0,1)<br />
2. Let x= -1/a*ln(1-u)<br />
<br />
Additional Example:<br />
Write an algorithm to generate a random variable from F(x)=x^12, 0<x<1<br />
Solution:<br />
1. Generate u~U(0,1)<br />
2. u=x^12<br />
x=u^(1/12)<br />
3. output x<br />
we need to show that [[Pi]] si the stationary distribution of this Markov Chain,<br />
[pi]=[pi]P<br />
detailed balance<br />
Remark 1;<br />
A common choice for q(y|x) is a normal distribution centered at X with standard deviation b q(y|x)= N (x, b^2) in this case q(y|x) is symmetric.</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49904stat940F212021-09-06T00:12:05Z<p>Aghodsib: Aghodsib moved page stat946F20 to stat940F21</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 2 || Jose Avilez || 1|| Gradientless Descent: High-Dimensional Zeroth-Order Optimisation || [https://openreview.net/pdf?id=Skep6TVYDB] || [[GradientLess Descent]] || [https://uofwaterloo-my.sharepoint.com/:v:/g/personal/jlavilez_uwaterloo_ca/ETNogDRpwJlPjSo5o0EY53UBLC7f0zmR9--a0uz6GYN8zw?e=J8V0f3 GLD Presentation] [[File:GradientLessDescent.pdf|Slides]] <br />
|-<br />
|Week of Nov 2 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 2 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 2 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 2 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 2 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:stat940F21&diff=49906Talk:stat940F212021-09-06T00:12:05Z<p>Aghodsib: Aghodsib moved page Talk:stat946F20 to Talk:stat940F21</p>
<hr />
<div>== SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems ==</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F20&diff=49905stat946F202021-09-06T00:12:05Z<p>Aghodsib: Aghodsib moved page stat946F20 to stat940F21</p>
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<div>#REDIRECT [[stat940F21]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:stat946F20&diff=49907Talk:stat946F202021-09-06T00:12:05Z<p>Aghodsib: Aghodsib moved page Talk:stat946F20 to Talk:stat940F21</p>
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<div>#REDIRECT [[Talk:stat940F21]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49900stat940F212021-09-06T00:11:30Z<p>Aghodsib: Aghodsib moved page STAT 940 F21 to stat946F20</p>
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<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 2 || Jose Avilez || 1|| Gradientless Descent: High-Dimensional Zeroth-Order Optimisation || [https://openreview.net/pdf?id=Skep6TVYDB] || [[GradientLess Descent]] || [https://uofwaterloo-my.sharepoint.com/:v:/g/personal/jlavilez_uwaterloo_ca/ETNogDRpwJlPjSo5o0EY53UBLC7f0zmR9--a0uz6GYN8zw?e=J8V0f3 GLD Presentation] [[File:GradientLessDescent.pdf|Slides]] <br />
|-<br />
|Week of Nov 2 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 2 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 2 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 2 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 2 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:stat940F21&diff=49902Talk:stat940F212021-09-06T00:11:30Z<p>Aghodsib: Aghodsib moved page Talk:STAT 940 F21 to Talk:stat946F20</p>
<hr />
<div>== SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems ==</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT_940_F21&diff=49901STAT 940 F212021-09-06T00:11:30Z<p>Aghodsib: Aghodsib moved page STAT 940 F21 to stat946F20</p>
<hr />
<div>#REDIRECT [[stat946F20]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:STAT_940_F21&diff=49903Talk:STAT 940 F212021-09-06T00:11:30Z<p>Aghodsib: Aghodsib moved page Talk:STAT 940 F21 to Talk:stat946F20</p>
<hr />
<div>#REDIRECT [[Talk:stat946F20]]</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=49898stat940F212021-09-06T00:02:16Z<p>Aghodsib: Aghodsib moved page stat946F20 to STAT 940 F21 without leaving a redirect</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 2 || Jose Avilez || 1|| Gradientless Descent: High-Dimensional Zeroth-Order Optimisation || [https://openreview.net/pdf?id=Skep6TVYDB] || [[GradientLess Descent]] || [https://uofwaterloo-my.sharepoint.com/:v:/g/personal/jlavilez_uwaterloo_ca/ETNogDRpwJlPjSo5o0EY53UBLC7f0zmR9--a0uz6GYN8zw?e=J8V0f3 GLD Presentation] [[File:GradientLessDescent.pdf|Slides]] <br />
|-<br />
|Week of Nov 2 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]]<br />
|-<br />
|Week of Nov 2 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]||<br />
|-<br />
|Week of Nov 2 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation]<br />
|-<br />
|Week of Nov 2 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] <br />
|-<br />
|Week of Nov 2 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video]<br />
|-<br />
|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] ||<br />
|-<br />
|Week of Nov 9 || Parsa Torabian || 8|| Orthogonal Gradient Descent for Continual Learning || [http://proceedings.mlr.press/v108/farajtabar20a/farajtabar20a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=orthogonal_gradient_descent_for_continual_learning Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Arash Moayyedi || 9|| When Does Self-supervision Improve Few-shot Learning? || [https://openreview.net/forum?id=HkenPn4KPH Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_Does_Self-Supervision_Improve_Few-Shot_Learning%3F Summary] || Learn<br />
|-<br />
|Week of Nov 9 || Parsa Ashrafi Fashi || 10|| Learning to Generalize: Meta-Learning for Domain Generalization || [https://arxiv.org/pdf/1710.03463 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Meta-Learning_For_Domain_Generalization Summary]|| [https://youtu.be/b9MU5cc3-m0 Presentation Video]<br />
|-<br />
|Week of Nov 9 || Jaskirat Singh Bhatia || 11|| A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION || [https://openreview.net/pdf?id=HygDF6NFPB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=a_fair_comparison_of_graph_neural_networks_for_graph_classification Summary] || [https://drive.google.com/file/d/1Dx6mFL_zBAJcfPQdOWAuPn0_HkvTL_0z/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 9 || Gaurav Sikri || 12|| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES || [https://openreview.net/pdf?id=HJxdTxHYvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Breaking_Certified_Defenses:_Semantic_Adversarial_Examples_With_Spoofed_Robustness_Certificates Summary] || [[https://drive.google.com/file/d/1amkWrR8ZQKnnInjedRZ7jbXTqCA8Hy1r/view?usp=sharing Presentation ]]<br />
|-<br />
|Week of Nov 16 || Abhinav Jain || 13|| The Logical Expressiveness of Graph Neural Networks || [http://www.openreview.net/pdf?id=r1lZ7AEKvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=THE_LOGICAL_EXPRESSIVENESS_OF_GRAPH_NEURAL_NETWORKS Summary] || [https://drive.google.com/file/d/1mZVlF2UvJ2lGjuVcN5SYqBuO4jZjuCcU/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Gautam Bathla || 14|| One-Shot Object Detection with Co-Attention and Co-Excitation || [https://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=One-Shot_Object_Detection_with_Co-Attention_and_Co-Excitation Summary] || [https://drive.google.com/file/d/1OUx64_pTZzCQAdo_fmy_9h9NbuccTnn6/view?usp=sharing Presentation]<br />
|-<br />
|Week of Nov 16 || Shikhar Sakhuja || 15|| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems || [https://papers.nips.cc/paper/8589-superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=SuperGLUE Summary] || [[https://youtu.be/5h-365TPQqE Presentation ]]<br />
|-<br />
|Week of Nov 16 || Cameron Meaney || 16|| Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations || [https://www.sciencedirect.com/science/article/pii/S0021999118307125 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Physics-informed_neural_networks:_A_deep_learning_framework_for_solving_forward_and_inverse_problems_involving_nonlinear_partial_differential_equations Summary] || Learn<br />
|-<br />
|Week of Nov 16 ||Sobhan Hemati|| 17||Adversarial Fisher Vectors for Unsupervised Representation Learning||[https://papers.nips.cc/paper/9295-adversarial-fisher-vectors-for-unsupervised-representation-learning.pdf Paper]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adversarial_Fisher_Vectors_for_Unsupervised_Representation_Learning Summary] || [https://www.youtube.com/watch?v=WKUj30tgHfs&feature=youtu.be video]<br />
|-<br />
|Week of Nov 16 ||Milad Sikaroudi|| 18||Domain Genralization via Model Agnostic Learning of Semantic Features||[https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Model_Agnostic_Learning_of_Semantic_Features Summary]|| [https://youtu.be/djrJG6pJaL0 video] also available on Learn<br />
|-<br />
|Week of Nov 23 ||Bowen You|| 19||DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION||[https://openreview.net/pdf?id=S1lOTC4tDS Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=DREAM_TO_CONTROL:_LEARNING_BEHAVIORS_BY_LATENT_IMAGINATION Summary] || Learn<br />
|-<br />
|Week of Nov 23 ||Nouha Chatti|| 20|| This Looks Like That: Deep Learning for Interpretable Image Recognition||[https://papers.nips.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=This_Looks_Like_That:_Deep_Learning_for_Interpretable_Image_Recognition#Source_code Summary] ||<br />
|-<br />
|Week of Nov 23 || Mohan Wu || 21|| Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/807-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Extreme_Multi-label_Text_Classification Summary] || [https://www.youtube.com/watch?v=jG57QgY71yU video]<br />
|-<br />
|Week of Nov 23 || Xinyi Yan || 22|| Dense Passage Retrieval for Open-Domain Question Answering || [https://arxiv.org/abs/2004.04906 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Dense_Passage_Retrieval_for_Open-Domain_Question_Answering Summary] || Learn<br />
|-<br />
|Week of Nov 23 || Meixi Chen || 23|| Functional Regularisation for Continual Learning with Gaussian Processes || [https://arxiv.org/pdf/1901.11356.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Functional_regularisation_for_continual_learning_with_gaussian_processes Summary]|| Learn<br />
|-<br />
|Week of Nov 23 || Ahmed Salamah || 24|| AdaCompress: Adaptive Compression for Online Computer Vision Services || [https://arxiv.org/pdf/1909.08148.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Adacompress:_Adaptive_compression_for_online_computer_vision_services Summary] || [https://youtu.be/D54qsSkqryk video] or Learn<br />
|-<br />
|Week of Nov 23|| Mohammad Mahmoud || 32||Mathematical Reasoning in Latent Space|| [https://iclr.cc/virtual_2020/poster_Ske31kBtPr.html?fbclid=IwAR2TQkabQkOzGcMl6bEJYggq8X8HIUoTudPIACX2v_ZT2LteARl_sPD-XdQ] || ||<br />
|-<br />
|Week of Nov 30 ||Danial Maleki || 25||RoBERTa: A Robustly Optimized BERT Pretraining Approach ||[https://openreview.net/forum?id=SyxS0T4tvS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Roberta Summary] || [https://youtu.be/JdfvvYbH-2s Presentation Video]<br />
|-<br />
|Week of Nov 30 ||Gursimran Singh || 26||BERTScore: Evaluating Text Generation with BERT||[https://openreview.net/pdf?id=SkeHuCVFDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=BERTScore:_Evaluating_Text_Generation_with_BERT Summary] || Learn<br />
|-<br />
|Week of Nov 30 || Govind Sharma || 27|| Time-series Generative Adversarial Networks || [https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Time-series_Generative_Adversarial_Networks Summary] || [https://youtu.be/SENjFF4N45s video] or Learn<br />
|-<br />
|Week of Nov 30 ||Maral Rasoolijaberi|| 28||A critical analysis of self-supervision, or what we can learn from a single image|| [https://openreview.net/pdf?id=B1esx6EYvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CRITICAL_ANALYSIS_OF_SELF-SUPERVISION Summary]|| [https://youtu.be/HkkacHrvloE YouTube]<br />
|-<br />
|Week of Nov 30 || Sina Farsangi || 29|| Self-Supervised Learning of Pretext-Invariant Representations || [https://openaccess.thecvf.com/content_CVPR_2020/papers/Misra_Self-Supervised_Learning_of_Pretext-Invariant_Representations_CVPR_2020_paper.pdf Paper]|| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Self-Supervised_Learning_of_Pretext-Invariant_Representations Summary] || [https://www.youtube.com/watch?v=IlIPHclzV5E&ab_channel=sinaebrahimifarsangi YouTube] or Learn<br />
|-<br />
|Week of Nov 30 || Pierre McWhannel || 30|| Pre-training Tasks for Embedding-based Large-scale Retrieval || [https://openreview.net/pdf?id=rkg-mA4FDr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pre-Training_Tasks_For_Embedding-Based_Large-Scale_Retrieval Summary]|| Learn<br />
|-<br />
|Week of Nov 30 || Wenjuan Qi || 31|| Network Deconvolution || [https://openreview.net/pdf?id=rkeu30EtvS Paper] || placeholder||</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=Talk:stat940F21&diff=49899Talk:stat940F212021-09-06T00:02:16Z<p>Aghodsib: Aghodsib moved page Talk:stat946F20 to Talk:STAT 940 F21 without leaving a redirect</p>
<hr />
<div>== SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems ==</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42593stat441F212020-10-07T02:57:26Z<p>Aghodsib: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="250pt"|Name <br />
|width="15pt"|Paper number <br />
|width="700pt"|Title<br />
|width="15pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks] || ||<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Netorks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| || || ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Emergent Tool Use From Multi-Agent Autocurricula || [https://arxiv.org/pdf/1909.07528.pdf] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42592stat441F212020-10-07T02:56:47Z<p>Aghodsib: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="200pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks] || ||<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Netorks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| || || ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Emergent Tool Use From Multi-Agent Autocurricula || [https://arxiv.org/pdf/1909.07528.pdf] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42591stat441F212020-10-07T02:56:05Z<p>Aghodsib: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks] || ||<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Netorks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| || || ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Emergent Tool Use From Multi-Agent Autocurricula || [https://arxiv.org/pdf/1909.07528.pdf] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| [https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42590stat441F212020-10-07T02:55:19Z<p>Aghodsib: /* Paper presentation */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 ||Sharman Bharat, Li Dylan,Lu Leonie, Li Mingdao || 1|| Risk prediction in life insurance industry using supervised learning algorithms || [https://rdcu.be/b780J Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=User:Bsharman Summary] ||<br />
|-<br />
|Week of Nov 16 || Delaney Smith, Mohammad Assem Mahmoud || 2|| Influenza Forecasting Framework based on Gaussian Processes || [https://proceedings.icml.cc/static/paper_files/icml/2020/1239-Paper.pdf] paper || ||<br />
|-<br />
|Week of Nov 16 || Tatianna Krikella, Swaleh Hussain, Grace Tompkins || 3|| Processing of Missing Data by Neural Networks || [http://papers.nips.cc/paper/7537-processing-of-missing-data-by-neural-networks] || ||<br />
|-<br />
|Week of Nov 16 ||Jonathan Chow, Nyle Dharani, Ildar Nasirov ||4 ||Streaming Bayesian Inference for Crowdsourced Classification ||[https://papers.nips.cc/paper/9439-streaming-bayesian-inference-for-crowdsourced-classification.pdf Paper] || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 ||Jinjiang Lian, Jiawen Hou, Yisheng Zhu, Mingzhe Huang || 11|| DROCC: Deep Robust One-Class Classification || [https://proceedings.icml.cc/static/paper_files/icml/2020/6556-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || Bushra Haque, Hayden Jones, Michael Leung, Cristian Mustatea || 12|| Combine Convolution with Recurrent Netorks for Text Classification || [https://arxiv.org/pdf/2006.15795.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || Qianlin Song, William Loh, Junyue Bai, Phoebe Choi || 14|| Task Understanding from Confusing Multi-task Data || [https://proceedings.icml.cc/static/paper_files/icml/2020/578-Paper.pdf paper] || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || Xiaolan Xu, Robin Wen, Yue Weng, Beizhen Chang || 16|| || || ||<br />
|-<br />
|Week of Nov 23 ||Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty || 17|| Emergent Tool Use From Multi-Agent Autocurricula || [https://arxiv.org/pdf/1909.07528.pdf] || ||<br />
|-<br />
|Week of Nov 23 ||Guanting Pan, Haocheng Chang, Zaiwei Zhang || 18|| Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence || [https://arxiv.org/pdf/1809.10770.pdf Paper] || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || Anas Mahdi Will Thibault Jan Lau Jiwon Yang || 25|| Loss Function Search for Face Recognition<br />
|| https://proceedings.icml.cc/static/paper_files/icml/2020/245-Paper.pdf || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || Bertrand Sodjahin, Junyi Yang, Jill Yu Chieh Wang, Yu Min Wu, Calvin Li || 30|| Research paper classifcation systems based on TF‑IDF and LDA schemes || [https://hcis-journal.springeropen.com/articles/10.1186/s13673-019-0192-7?fbclid=IwAR3swO-eFrEbj1BUQfmomJazxxeFR6SPgr6gKayhs38Y7aBG-zX1G3XWYRM Paper] || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=42506stat940F212020-10-04T21:17:26Z<p>Aghodsib: /* Paper presentation */</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 2 || || 1|| || || ||<br />
|-<br />
|Week of Nov 2 || || 2|| || || ||<br />
|-<br />
|Week of Nov 2 || || 3|| || || ||<br />
|-<br />
|Week of Nov 2 || || 4|| || || ||<br />
|-<br />
|Week of Nov 2 || || 5|| || || ||<br />
|-<br />
|Week of Nov 2 || || 6|| || || ||<br />
|-<br />
|Week of Nov 9 || || 7|| || || ||<br />
|-<br />
|Week of Nov 9 || || 8|| || || ||<br />
|-<br />
|Week of Nov 9 || || 9|| || || ||<br />
|-<br />
|Week of Nov 9 || || 10|| || || ||<br />
|-<br />
|Week of Nov 9 || || 11|| || || ||<br />
|-<br />
|Week of Nov 9 || || 12|| || || ||<br />
|-<br />
|Week of Nov 16 || || 13|| || || ||<br />
|-<br />
|Week of Nov 16 || || 14|| || || ||<br />
|-<br />
|Week of Nov 16 || || 15|| || || ||<br />
|-<br />
|Week of Nov 16 || || 16|| || || ||<br />
|-<br />
|Week of Nov 16 || || 17|| || || ||<br />
|-<br />
|Week of Nov 16 || || 18|| || || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 23 || || 21|| || || ||<br />
|-<br />
|Week of Nov 23 || || 22|| || || ||<br />
|-<br />
|Week of Nov 23 || || 23|| || || ||<br />
|-<br />
|Week of Nov 23 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || || 25|| || || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || || 30|| || || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=42505stat940F212020-10-04T21:13:28Z<p>Aghodsib: /* Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing] */</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || || 1|| || || ||<br />
|-<br />
|Week of Nov 16 || || 2|| || || ||<br />
|-<br />
|Week of Nov 16 || || 3|| || || ||<br />
|-<br />
|Week of Nov 16 || || 4|| || || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 || || 11|| || || ||<br />
|-<br />
|Week of Nov 23 || || 12|| || || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || || 14|| || || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || || 16|| || || ||<br />
|-<br />
|Week of Nov 23 || || 17|| || || ||<br />
|-<br />
|Week of Nov 23 || || 18|| || || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || || 25|| || || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || || 30|| || || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=42504stat940F212020-10-04T21:12:42Z<p>Aghodsib: /* Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit#gid=0] */</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=42503stat940F212020-10-04T21:10:04Z<p>Aghodsib: /* Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing] */</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/1Me_O000pNxeTwNGEac57XakecG1wahvwGE5n36DGIlM/edit#gid=0]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42502stat441F212020-10-04T21:02:24Z<p>Aghodsib: /* Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing] */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
|-<br />
|Week of Nov 16 || || 1|| || || ||<br />
|-<br />
|Week of Nov 16 || || 2|| || || ||<br />
|-<br />
|Week of Nov 16 || || 3|| || || ||<br />
|-<br />
|Week of Nov 16 || || 4|| || || ||<br />
|-<br />
|Week of Nov 16 || || 5|| || || ||<br />
|-<br />
|Week of Nov 16 || || 6|| || || ||<br />
|-<br />
|Week of Nov 16 || || 7|| || || ||<br />
|-<br />
|Week of Nov 16 || || 8|| || || ||<br />
|-<br />
|Week of Nov 16 || || 9|| || || ||<br />
|-<br />
|Week of Nov 16 || || 10|| || || ||<br />
|-<br />
|Week of Nov 23 || || 11|| || || ||<br />
|-<br />
|Week of Nov 23 || || 12|| || || ||<br />
|-<br />
|Week of Nov 23 || || 13|| || || ||<br />
|-<br />
|Week of Nov 23 || || 14|| || || ||<br />
|-<br />
|Week of Nov 23 || || 15|| || || ||<br />
|-<br />
|Week of Nov 23 || || 16|| || || ||<br />
|-<br />
|Week of Nov 23 || || 17|| || || ||<br />
|-<br />
|Week of Nov 23 || || 18|| || || ||<br />
|-<br />
|Week of Nov 23 || || 19|| || || ||<br />
|-<br />
|Week of Nov 23 || || 20|| || || ||<br />
|-<br />
|Week of Nov 30 || || 21|| || || ||<br />
|-<br />
|Week of Nov 30 || || 22|| || || ||<br />
|-<br />
|Week of Nov 30 || || 23|| || || ||<br />
|-<br />
|Week of Nov 30 || || 24|| || || ||<br />
|-<br />
|Week of Nov 30 || || 25|| || || ||<br />
|-<br />
|Week of Nov 30 || || 26|| || || ||<br />
|-<br />
|Week of Nov 30 || || 27|| || || ||<br />
|-<br />
|Week of Nov 30 || || 28|| || || ||<br />
|-<br />
|Week of Nov 30 || || 29|| || || ||<br />
|-<br />
|Week of Nov 30 || || 30|| || || ||<br />
|-</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=42501stat940F212020-10-04T21:02:08Z<p>Aghodsib: </p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==<br />
<br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=F21-STAT_940-Proposal&diff=42500F21-STAT 940-Proposal2020-10-04T21:01:13Z<p>Aghodsib: Created page with "Use this format (Don’t remove Project 0) Project # 0 Group members: Last name, First name Last name, First name Last name, First name Last name, First name Title: Maki..."</p>
<hr />
<div>Use this format (Don’t remove Project 0)<br />
<br />
Project # 0 Group members:<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Last name, First name<br />
<br />
Title: Making a String Telephone<br />
<br />
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).<br />
<br />
Project # 1 Group members:</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat940F21&diff=42499stat940F212020-10-04T21:00:45Z<p>Aghodsib: Created page with "== Project Proposal =="</p>
<hr />
<div>== [[F20-STAT 946-Proposal| Project Proposal ]] ==</div>Aghodsibhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F21&diff=42498stat441F212020-10-03T16:52:34Z<p>Aghodsib: /* Project Proposal */</p>
<hr />
<div><br />
<br />
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==<br />
<br />
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--><br />
<br />
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=<br />
<br />
Use the following notations:<br />
<br />
P: You have written a summary/critique on the paper.<br />
<br />
T: You had a technical contribution on a paper (excluding the paper that you present).<br />
<br />
E: You had an editorial contribution on a paper (excluding the paper that you present).<br />
<br />
<br />
<br />
<br />
=Paper presentation=<br />
{| class="wikitable"<br />
<br />
{| border="1" cellpadding="3"<br />
|-<br />
|width="60pt"|Date<br />
|width="100pt"|Name <br />
|width="30pt"|Paper number <br />
|width="700pt"|Title<br />
|width="30pt"|Link to the paper<br />
|width="30pt"|Link to the summary<br />
|width="30pt"|Link to the video<br />
|-<br />
|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539]<br />
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|-</div>Aghodsib