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|Nov 13 || Tim Tse || || . From machine learning to machine reasoning. Mach. Learn. ||[http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper]|| | |Nov 13 || Tim Tse || || . From machine learning to machine reasoning. Mach. Learn. ||[http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper]|| | ||
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|Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || | |Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || [[NeuralMachineTranslation_JointAlignTranslate|Summary]] | ||
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|Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]|| [[Deep neural networks for acoustic modeling in speech recognition| Summary]] | |Nov 13 || Abdullah Rashwan || || Deep neural networks for acoustic modeling in speech recognition. ||[http://research.microsoft.com/pubs/171498/HintonDengYuEtAl-SPM2012.pdf paper]|| [[Deep neural networks for acoustic modeling in speech recognition| Summary]] |
Revision as of 15:44, 5 November 2015
List of Papers
Record your contributions here:
Use the following notations:
S: You have written a summary on the paper
T: You had technical contribution on a paper (excluding the paper that you present from set A or critique from set B)
E: You had editorial contribution on a paper (excluding the paper that you present from set A or critique from set B)
Your feedback on presentations
Set A
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 | Ali Ghodsi | Lecturer | |||
Oct 23 | Ali Ghodsi | 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 | Ali Ghodsi | Lecturer | |||
Oct 30 | Ali Ghodsi | Lecturer | |||
Oct 30 | Rui Qiao | Going deeper with convolutions | Paper | Summary | |
Oct 30 | Amirreza Lashkari | 21 | Overfeat: integrated recognition, localization and detection using convolutional networks. | Paper | Summary |
Mkeup Class (TBA) | Peter Blouw | Memory Networks. | [1] | Summary | |
Nov 6 | Ali Ghodsi | Lecturer | |||
Nov 6 | Ali Ghodsi | Lecturer | |||
Nov 6 | Anthony Caterini | 56 | Human-level control through deep reinforcement learning | Paper | Summary |
Nov 6 | Sean Aubin | Learning Hierarchical Features for Scene Labeling | Paper | Summary | |
Nov 13 | 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 | Summary | |
Nov 13 | Abdullah Rashwan | Deep neural networks for acoustic modeling in speech recognition. | paper | Summary | |
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 | summary | |
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 | Ali Sarhadi | Strategies for Training Large Scale Neural Network Language Models | |||
Dec 4 | Chris Choi | On the difficulty of training recurrent neural networks | Paper | Summary | |
Dec 4 | Fatemeh Karimi | MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION | Paper | ||
Dec 4 | Jan Gosmann | A fast learning algorithm for deep belief nets | Paper | Summary | |
Dec 4 | Dylan Drover | Towards AI-complete question answering: a set of prerequisite toy tasks | Paper |
Set B
Name | Paper number | Title | Link to the paper | Link to the summary |
Anthony Caterini | 15 | The Manifold Tangent Classifier | Paper | |
Jan Gosmann | Neural Turing machines | Paper | Summary | |
Brent Komer | Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers | Paper | ||
Sean Aubin | Deep Sparse Rectifier Neural Networks | Paper | Summary | |
Peter Blouw | Generating text with recurrent neural networks | Paper | ||
Tim Tse | Question answering with subgraph embeddings | Paper |