Difference between revisions of "stat441F20"

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(Created page with " == Project Proposal == [https://goo.gl/forms/apurag4dr9kSR76X2 Your feedback on presentations] = Record your contributions here [ht...")
 
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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]
 
|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]
 
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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]
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|Nov 16 ||   || 1|| ||   ||  
 
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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]
 
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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] ||
 
[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]  ||
 
[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]
 
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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]
 
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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]
 
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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]
 
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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]
 
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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]
 
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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]
 
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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]
 
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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]
 
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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]
 
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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]
 

Revision as of 11:46, 5 September 2020


Project Proposal

Your feedback on presentations


Record your contributions here [1]

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

Date Name Paper number Title Link to the paper Link to the summary
Feb 15 (example) Ri Wang Sequence to sequence learning with neural networks. Paper Summary
Nov 16 1