# Difference between revisions of "stat441F21"

From statwiki

(→Paper presentation) |
|||

(275 intermediate revisions by 91 users not shown) | |||

Line 4: | Line 4: | ||

<!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--> | <!--[https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations]--> | ||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

=Paper presentation= | =Paper presentation= | ||

Line 30: | Line 20: | ||

|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] | |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] | ||

|- | |- | ||

− | |Week of Nov 16 || | + | |Week of Nov 16 || Ali Ghodsi || || || || || |

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

− | |||

|- | |- | ||

− | |Week of Nov | + | |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 | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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 | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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 | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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 | + | |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 | + | |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]|| |

|- | |- | ||

− | |||

|- | |- | ||

− | |Week of Nov | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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] || |

|- | |- | ||

− | |Week of Nov | + | |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] || |

− | |||

|- | |- | ||

− | |Week of Nov | + | |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]|| |

|- | |- | ||

− | |Week of Nov | + | |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]|| |

|- | |- | ||

− | |Week of Nov | + | |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]|| |

|- | |- | ||

− | |Week of Nov | + | |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]|| |

|- | |- | ||

− | |Week of Nov | + | |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 ||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 |