Difference between revisions of "f15Stat946PaperSignUp"
From statwiki
Line 47:  Line 47:  
    
Nov 20  Luyao Ruan   Dropout: A Simple Way to Prevent Neural Networks from Overfitting  [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]  Nov 20  Luyao Ruan   Dropout: A Simple Way to Prevent Neural Networks from Overfitting  [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]  
+    
+  Nov 20       
    
Nov 27 Mahmood Gohari  Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships [http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]  Nov 27 Mahmood Gohari  Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships [http://pubs.acs.org/doi/abs/10.1021/ci500747n.pdf Paper]  
Line 53:  Line 55:  
    
Nov 27 Xinran Liu  ImageNet Classification with Deep Convolutional Neural Networks [http://papers.nips.cc/paper/4824imagenetclassificationwithdeepconvolutionalneuralnetworks.pdf Paper][[ImageNet Classification with Deep Convolutional Neural NetworksSummary]]  Nov 27 Xinran Liu  ImageNet Classification with Deep Convolutional Neural Networks [http://papers.nips.cc/paper/4824imagenetclassificationwithdeepconvolutionalneuralnetworks.pdf Paper][[ImageNet Classification with Deep Convolutional Neural NetworksSummary]]  
+    
+  Nov 27       
    
Dec 4  Chris Choi   On the difficulty of training recurrent neural networks  [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper]  [[On the difficulty of training recurrent neural networks  Summary]]  Dec 4  Chris Choi   On the difficulty of training recurrent neural networks  [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper]  [[On the difficulty of training recurrent neural networks  Summary]] 
Revision as of 14:31, 9 October 2015
Date  Name  Paper number  Title  Link to the paper  Link to the summary 
Oct 16  pascal poupart  Guest Lecturer  
Oct 16  pascal poupart  Guest Lecturer  
Oct 23  Ri Wang  Sequence to sequence learning with neural networks.  Paper  
Oct 23  Deepak Rishi  Parsing natural scenes and natural language with recursive neural networks  Paper  
Oct 30  Rui Qiao  Going deeper with convolutions  Paper  Summary  
Oct 30  Amirreza Lashkari  Overfeat: integrated recognition, localization and detection using convolutional networks.  Paper  Summary  
Oct 30  Peter Blouw  Distributed representations of words and phrases and their compositionality.  [1]  Summary  
Nov 6  Anthony Caterini  Humanlevel control through deep reinforcement learning  Paper  Summary  
Nov 6  Sean Aubin  Learning Hierarchical Features for Scene Labeling  Paper  Summary  
Nov 6  Mike Hynes  12  Speech recognition with deep recurrent neural networks  Paper  Summary

Nov 13  Tim Tse  . From machine learning to machine reasoning. Mach. Learn.  Paper  
Nov 13  Maysum Panju  Neural machine translation by jointly learning to align and translate  Paper  
Nov 13  Abdullah Rashwan  Deep neural networks for acoustic modeling in speech recognition.  paper  
Nov 20  Valerie Platsko  Natural language processing (almost) from scratch.  Paper  
Nov 20  Brent Komer  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention  Paper  Summary  
Nov 20  Luyao Ruan  Dropout: A Simple Way to Prevent Neural Networks from Overfitting  Paper  
Nov 20  
Nov 27  Mahmood Gohari  Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships  Paper  
Nov 27  Derek Latremouille  The WakeSleep Algorithm for Unsupervised Neural Networks  Paper  
Nov 27  Xinran Liu  ImageNet Classification with Deep Convolutional Neural Networks  Paper  Summary  
Nov 27  
Dec 4  Chris Choi  On the difficulty of training recurrent neural networks  Paper  Summary  
Dec 4  Fatemeh Karimi  Connectomic reconstruction of the inner plexiform layer in the mouse retina  Paper  
Dec 4  Jan Gosmann  A fast learning algorithm for deep belief nets  Paper  Summary  
Dec 4 