f15Stat946PaperSignUp: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
m (Paper signup for Peter Blouw) |
||
| Line 27: | Line 27: | ||
|- | |- | ||
|Oct 30 ||Amirreza Lashkari|| ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]] | |Oct 30 ||Amirreza Lashkari|| ||Overfeat: integrated recognition, localization and detection using convolutional networks. || [http://arxiv.org/pdf/1312.6229v4.pdf Paper]|| [[Overfeat: integrated recognition, localization and detection using convolutional networks|Summary]] | ||
|- | |||
|Oct 30 || Peter Blouw|| ||Distributed representations of words and phrases and their compositionally.|| [http://goo.gl/NCXliI]|| [[Distributed representations of words and phrases and their compositionally|Summary]] | |||
|- | |- | ||
|Nov 6 || Anthony Caterini || || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]] | |Nov 6 || Anthony Caterini || || Human-level control through deep reinforcement learning ||[http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf Paper]|| [[Human-level control through deep reinforcement learning|Summary]] | ||
Revision as of 16:44, 5 October 2015
| Date | Name | Paper number | Title | Link to the paper | Link to the summary |
| Example | Ali Ghodsi | 1 | Deep sparse rectifier neural networks. | Paper | 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 compositionally. | [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 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 | |
| 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 |