# Difference between revisions of "stat441F21"

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|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] || | |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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− | |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] || || | + | |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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|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] || | |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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|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]|| | |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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− | |Week of Nov 29 ||Junbin Pan|| || Wide & Deep Learning for Recommender Systems || [https://arxiv.org/pdf/1606.07792v1.pdf Paper] || [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 03:28, 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 |