Difference between revisions of "stat441F21"

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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] ||
 
|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 22 || 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 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 ||  || || || ||
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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] || ||
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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] || ||
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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 ||  || || || ||
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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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|Week of Nov 29 || Eric Anderson, Chengzhi Wang, Kai Zhong, YiJing Zhou || || || || ||
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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] ||
 
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|Week of Nov 29 || Ethan Cyrenne, Dieu Hoa Nguyen, Mary Jane Sin, Carolyn Wang || || || || ||
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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] || [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 || Chun Waan Loke, Peter Chong, Clarice Osmond, Zhilong Li|| || || || ||
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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] ||
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|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] ||
 
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|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] ||
 
|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] ||
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|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] ||
 
|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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|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] || ||
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|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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|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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|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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|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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|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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|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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|Week of Nov 29 || Kar Lok Ng, Muhan (Iris) Li || || || || ||
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|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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|Week of Nov 29 ||Kun Wang || || || || ||
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|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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|Week of Nov 22 ||Bsodjahi || || Bayesian Network as a Decision Tool for Predicting ALS Disease || https://www.mdpi.com/2076-3425/11/2/150/pdf || ||
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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]||
 
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|Week of Nov 22 ||Xin yan, Yishu Duan, Xibei Di || || Predicting Hurricane Trajectories Using a Recurrent Neural Network || || ||
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|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