Difference between revisions of "f15Stat946PaperSignUp"

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|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]||
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|Nov 20 ||  || ||  || ||
 
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|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]||
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|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]
 
|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]]
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|Nov 27 ||  || ||  || ||
 
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|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

List of Papers


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 Human-level 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 Wake-Sleep 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