f15Stat946PaperSignUp: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
Line 20: | Line 20: | ||
|Oct 23 ||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] | |Oct 23 ||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] | ||
|- | |- | ||
|Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] || | |Oct 23 || Deepak Rishi || || Parsing natural scenes and natural language with recursive neural networks || [http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]] | ||
|- | |- | ||
|Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[Going deeper with convolutions|Summary]] | |Oct 30 ||Rui Qiao || ||Going deeper with convolutions || [http://arxiv.org/pdf/1409.4842v1.pdf Paper]|| [[Going deeper with convolutions|Summary]] |
Revision as of 15:58, 16 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 | Summary | |
Oct 23 | Deepak Rishi | Parsing natural scenes and natural language with recursive neural networks | Paper | Summary | |
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 | Ali Mahdipour | The human splicing code reveals new insights into the genetic determinants of disease | Paper | ||
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 |