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== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==
== [[F20-STAT 441/841 CM 763-Proposal| Project Proposal ]] ==


[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]
<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]-->
 
 
= Record your contributions here [https://docs.google.com/spreadsheets/d/10CHiJpAylR6kB9QLqN7lZHN79D9YEEW6CDTH27eAhbQ/edit?usp=sharing]=
 
Use the following notations:
 
P: You have written a summary/critique on the paper.
 
T: You had a technical contribution on a  paper (excluding the paper that you present).
 
E: You had an editorial contribution on a  paper (excluding the paper that you present).
 
 
 


=Paper presentation=
=Paper presentation=
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|-
|width="60pt"|Date
|width="60pt"|Date
|width="100pt"|Name  
|width="250pt"|Name  
|width="30pt"|Paper number  
|width="15pt"|Paper number  
|width="700pt"|Title
|width="700pt"|Title
|width="30pt"|Link to the paper
|width="15pt"|Link to the paper
|width="30pt"|Link to the summary
|width="30pt"|Link to the summary
|width="30pt"|Link to the video
|-
|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]
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|Week of Nov 16 || Ali Ghodsi || ||  ||  || ||
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|Week of Nov 22 || Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu|| || Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification || [http://proceedings.mlr.press/v139/bai21c/bai21c.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Don%27t_Just_Blame_Over-parametrization Summary] ||
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|Week of Nov 29 || Kanika Chopra, Yush Rajcoomar ||  || Automatic Bank Fraud Detection Using Support Vector Machines || [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.863.5804&rep=rep1&type=pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Automatic_Bank_Fraud_Detection_Using_Support_Vector_Machines Summary] ||
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|Week of Nov 22 || Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min ||  || Do Vision Transformers See Like Convolutional Neural Networks? || [https://proceedings.neurips.cc/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Do_Vision_Transformers_See_Like_CNN Summary] ||
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|Week of Nov 22 || Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell || || A Probabilistic Approach to Neural Network Pruning || [http://proceedings.mlr.press/v139/qian21a/qian21a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_of_A_Probabilistic_Approach_to_Neural_Network_Pruning Summary] ||
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|Week of Nov 22 || Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans || || Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification || [https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hou_Patch-Based_Convolutional_Neural_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Patch_Based_Convolutional_Neural_Network_for_Whole_Slide_Tissue_Image_Classification Summary] ||
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|Week of Nov 29 || Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew ||  || CatBoost: unbiased boosting with categorical features || [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=CatBoost:_unbiased_boosting_with_categorical_features Summary] ||
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|Feb 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] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary]
|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks || [https://arxiv.org/pdf/1804.00792.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Poison_Frogs_Neural_Networks Summary] ||
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|Nov 13 || Jason Schneider, Jordyn Walton, Zahraa Abbas, Andrew Na  || 1|| Memory-Based Parameter Adaptation || [https://arxiv.org/pdf/1802.10542.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Memory-Based_Parameter_Adaptation#Incremental_Learning Summary]
|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || Deep Residual Learning for Image Recognition || [https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Residual_Learning_for_Image_Recognition_Summary Summary] ||
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|Nov 13 ||Sai Praneeth M, Xudong Peng, Alice Li, Shahrzad Hosseini Vajargah|| 2|| Going Deeper with Convolutions  ||[https://arxiv.org/pdf/1409.4842.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary]
|Week of Nov 29 || Bowen Zhang, Tyler Magnus Verhaar, Sam Senko || || Deep Double Descent: Where Bigger Models and More Data Hurt || [https://arxiv.org/pdf/1912.02292.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Double_Descent_Where_Bigger_Models_and_More_Data_Hurt Summary] ||
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|NOv 15 || Yan Yu Chen, Qisi Deng, Hengxin Li,  Bochao Zhang|| 3|| Topic Compositional Neural Language Model|| [https://arxiv.org/pdf/1712.09783.pdf paper] ||  
|Week of Nov 29 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || XGBoost: A Scalable Tree Boosting System || [https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=XGBoost Summary] ||
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F18/TCNLM Summary]
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|Nov 15 ||   Zhaoran Hou, Pei Wei Wang, Chi Zhang, Yiming Li, Daoyi Chen, Ying Chi|| 4|| Extreme Learning Machine for regression and Multi-class Classification|| [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6035797 Paper] ||  
|Week of Nov 22 || Ann Gie Wong, Curtis Li, Hannah Kerr || || The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks || [https://www.mdpi.com/2079-9292/9/12/2178/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Detection_of_Black_Ice_Accidents_Using_CNNs&fbclid=IwAR0K4YdnL_hdRnOktmJn8BI6-Ra3oitjJof0YwluZgUP1LVFHK5jyiBZkvQ Summary] ||
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat841F18/ Summary]
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|NOv 20 || Kristi Brewster, Isaac McLellan, Ahmad Nayar Hassan, Marina Medhat Rassmi Melek, Brendan Ross, Jon Barenboim, Junqiao Lin, James Bootsma || 5|| A Neural Representation of Sketch Drawings || [https://arxiv.org/pdf/1704.03477.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Summary_-_A_Neural_Representation_of_Sketch_Drawings Summary]  
|Week of Nov 22 || Yuwei Liu, Daniel Mao|| || Depthwise Convolution Is All You Need for Learning Multiple Visual Domains || [https://arxiv.org/abs/1902.00927 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Depthwise_Convolution_Is_All_You_Need_for_Learning_Multiple_Visual_Domains Summary] ||
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|Nov 20 || Maya(Mahdiyeh) Bayati, Saber Malekmohammadi, Vincent Loung || 6|| Convolutional Neural Networks for Sentence Classification || [https://arxiv.org/pdf/1408.5882.pdf paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_Neural_Networks_for_Sentence_Classi%EF%AC%81cation Summary]  
|Week of Nov 29 || Lingshan Wang, Yifan Li, Ziyi Liu || || Deep Learning for Extreme Multi-label Text Classification || [https://dl.acm.org/doi/pdf/10.1145/3077136.3080834 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Extreme_Multi-label_Text_Classification Summary]||
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|NOv 22 || Qingxi Huo, Yanmin Yang, Jiaqi Wang, Yuanjing Cai, Colin Stranc, Philomène Bobichon, Aditya Maheshwari, Zepeng An  || 7|| Robust Probabilistic Modeling with Bayesian Data Reweighting || [http://proceedings.mlr.press/v70/wang17g/wang17g.pdf Paper]  || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Robust_Probabilistic_Modeling_with_Bayesian_Data_Reweighting Summary]
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|Nov 22 ||   Hanzhen Yang, Jing Pu Sun, Ganyuan Xuan, Yu Su, Jiacheng Weng, Keqi Li, Yi Qian, Bomeng Liu || 8|| Deep Residual Learning for Image Recognition || [http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Residual_Learning_for_Image_Recognition Summary]
|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || Robust Imitation Learning from Noisy Demonstrations || [http://proceedings.mlr.press/v130/tangkaratt21a/tangkaratt21a.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Robust_Imitation_Learning_from_Noisy_Demonstrations Summary] ||
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|NOv 27 || Mitchell Snaith || 9|| You Only Look Once: Unified, Real-Time Object Detection || [https://arxiv.org/pdf/1506.02640.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat441F18/YOLO Summary]
|Week of Nov 29 ||Kun Wang || || Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases|| [https://doi-org.proxy.lib.uwaterloo.ca/10.1111/exsy.12705 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_neural_network_for_diagnosis_of_viral_pneumonia_and_COVID-19_alike_diseases Summary] ||
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|Nov 27 ||   Qi Chu, Xiaoran Huang, Di Sang, Amanda Lam, Yan Jiao, Shuyue Wang, Yutong Wu || 10|| 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=A_Brief_Survey_of_Text_Mining:_Classification,_Clustering_and_Extraction_Techniques Summary]
|Week of Nov 29 ||Egemen Guray || || Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network || [https://www.researchgate.net/publication/344399165_Traffic_Sign_Recognition_System_TSRS_SVM_and_Convolutional_Neural_Network Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Traffic_Sign_Recognition_System_(TSRS):_SVM_and_Convolutional_Neural_Network Summary] ||
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|NOv 29 || Jameson Ngo, Amy Xu, Aden Grant, Yu Hao Wang, Andrew McMurry, Baizhi Song, Yongqi Dong || 11|| Towards Deep Learning Models Resistant to Adversarial Attacks || [https://openreview.net/pdf?id=rJzIBfZAb Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Towards_Deep_Learning_Models_Resistant_to_Adversarial_Attacks Summary]
|Week of Nov 29 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease ||[https://www.mdpi.com/2076-3425/11/2/150/htm Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Bayesian_Network_as_a_Decision_Tool_for_Predicting_ALS_Disease Summary]||
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|Nov 29 || Qianying Zhao, Hui Huang, Lingyun Yi, Jiayue Zhang, Siao Chen, Rongrong Su, Gezhou Zhang, Meiyu Zhou  || 12|| XGBoost: A Scalable Tree Boosting System || [https://wiki.math.uwaterloo.ca/statwiki/images/9/9f/paper_presentation.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=XGBoost:_A_Scalable_Tree_Boosting_System Summary]
|Week of Nov 29 ||Xin Yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || [https://arxiv.org/pdf/1802.02548.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Predicting_Hurricane_Trajectories_Using_a_Recurrent_Neural_Network Summary]||
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|Nov 28 || Hudson Ash, Stephen Kingston, Richard Zhang, Alexandre Xiao, Ziqiu Zhu  || 13 || Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness || [https://arxiv.org/pdf/1608.05842.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Unsupervised_Learning_of_Optical_Flow_via_Brightness_Constancy_and_Motion_Smoothness Summary]
|Week of Nov 29 ||Ankitha Anugu, Yushan Chen, Yuying Huang || || A Game Theoretic Approach to Class-wise Selective Rationalization || [https://arxiv.org/pdf/1910.12853.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=A_Game_Theoretic_Approach_to_Class-wise_Selective_Rationalization#How_does_CAR_work_intuitively Summary]||
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|Nov 21 || Frank Jiang, Yuan Zhang, Jerry Hu  || 14 || Distributed Representations of Words and Phrases and their Compositionality || [https://arxiv.org/pdf/1310.4546.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Representations_of_Words_and_Phrases_and_their_Compositionality Summary]
|Week of Nov 29 ||Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi || || Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree || [https://www.hindawi.com/journals/sp/2021/5560465/ Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Research_on_Multiple_Classification_Based_on_Improved_SVM_Algorithm_for_Balanced_Binary_Decision_Tree Summary]||
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|Nov 21 || Yu Xuan Lee, Tsen Yee Heng  || 15 || Gradient Episodic Memory for Continual Learning || [http://papers.nips.cc/paper/7225-gradient-episodic-memory-for-continual-learning.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Gradient_Episodic_Memory_for_Continual_Learning Summary]
|Week of Nov 29 ||Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli|| || U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging|| [https://proceedings.neurips.cc/paper/2019/file/57bafb2c2dfeefba931bb03a835b1fa9-Paper.pdf?fbclid=IwAR1dZpx9vU1pSPTSm_nwk6uBU7TYJ2HNTrsqjaH-9ZycE_PFpFjJoHg1zhQ]||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=U-Time:A_Fully_Convolutional_Network_for_Time_Series_Segmentation_Applied_to_Sleep_Staging_Summary]||
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|Nov 28 || Ben Zhang, Rees Simmons, Sunil Mall  || 16 || Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift  || [https://arxiv.org/pdf/1502.03167.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Batch_Normalization Summary]
|Week of Nov 29 ||Junbin Pan|| || Wide & Deep Learning for Recommender Systems || [https://arxiv.org/pdf/1606.07792v1.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Wide_and_Deep_Learning_for_Recommender_Systems Summary]||

Latest revision as of 04:28, 1 December 2021


Project Proposal

Paper presentation

Date Name Paper number Title Link to the paper Link to the summary Link to the video
Sep 15 (example) Ri Wang Sequence to sequence learning with neural networks. Paper Summary [1]
Week of Nov 16 Ali Ghodsi
Week of Nov 22 Jared Feng, Xipeng Huang, Mingwei Xu, Tingzhou Yu Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification Paper Summary
Week of Nov 29 Kanika Chopra, Yush Rajcoomar Automatic Bank Fraud Detection Using Support Vector Machines Paper Summary
Week of Nov 22 Zeng Mingde, Lin Xiaoyu, Fan Joshua, Rao Chen Min Do Vision Transformers See Like Convolutional Neural Networks? Paper Summary
Week of Nov 22 Justin D'Astous, Waqas Hamed, Stefan Vladusic, Ethan O'Farrell A Probabilistic Approach to Neural Network Pruning Paper Summary
Week of Nov 22 Cassandra Wong, Anastasiia Livochka, Maryam Yalsavar, David Evans Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification Paper Summary
Week of Nov 29 Jessie Man Wai Chin, Yi Lin Ooi, Yaqi Shi, Shwen Lyng Ngew CatBoost: unbiased boosting with categorical features Paper Summary
Week of Nov 29 Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks Paper Summary
Week of Nov 29 Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang Deep Residual Learning for Image Recognition Paper Summary
Week of Nov 29 Bowen Zhang, Tyler Magnus Verhaar, Sam Senko Deep Double Descent: Where Bigger Models and More Data Hurt Paper Summary
Week of Nov 29 Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li XGBoost: A Scalable Tree Boosting System Paper Summary
Week of Nov 22 Ann Gie Wong, Curtis Li, Hannah Kerr The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks Paper Summary
Week of Nov 22 Yuwei Liu, Daniel Mao Depthwise Convolution Is All You Need for Learning Multiple Visual Domains Paper Summary
Week of Nov 29 Lingshan Wang, Yifan Li, Ziyi Liu Deep Learning for Extreme Multi-label Text Classification Paper Summary
Week of Nov 29 Kar Lok Ng, Muhan (Iris) Li Robust Imitation Learning from Noisy Demonstrations Paper Summary
Week of Nov 29 Kun Wang Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases Paper Summary
Week of Nov 29 Egemen Guray Traffic Sign Recognition System (TSRS): SVM and Convolutional Neural Network Paper Summary
Week of Nov 29 Bsodjahi Bayesian Network as a Decision Tool for Predicting ALS Disease Paper Summary
Week of Nov 29 Xin Yan, Yishu Duan, Xibei Di Predicting Hurricane Trajectories Using a Recurrent Neural Network Paper Summary
Week of Nov 29 Ankitha Anugu, Yushan Chen, Yuying Huang A Game Theoretic Approach to Class-wise Selective Rationalization Paper Summary
Week of Nov 29 Aavinash Syamala, Dilmeet Malhi, Sohan Islam, Vansh Joshi Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree Paper Summary
Week of Nov 29 Christian Mitrache, Alexandra Mossman, Jessica Saini, Aaron Renggli U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging [2] [3]
Week of Nov 29 Junbin Pan Wide & Deep Learning for Recommender Systems Paper Summary