# 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]|| [[dropout | Summary]] | |Nov 20 || Luyao Ruan || || Dropout: A Simple Way to Prevent Neural Networks from Overfitting || [https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf Paper]|| [[dropout | Summary]] | ||

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− | |Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] || [[ | + | |Nov 20 || Ali Mahdipour || || The human splicing code reveals new insights into the genetic determinants of disease ||[https://www.sciencemag.org/content/347/6218/1254806.full.pdf Paper] || [[Genetic Application of Deep Learning | Summary]] |

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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]|| |

## Revision as of 12:02, 16 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 | Question Answering with Subgraph Embeddings | Paper | Summary | |

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 | Summary | |

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 | Summary | |

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 | On the Number of Linear Regions of Deep Neural Networks | 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 | Summary | |

Sean Aubin | Deep Sparse Rectifier Neural Networks | Paper | Summary | |

Peter Blouw | Generating text with recurrent neural networks | Paper | ||

Tim Tse | From Machine Learning to Machine Reasoning | Paper | Summary | |

Rui Qiao | Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation | Paper | Summary | |

Ftemeh Karimi | 23 | Very Deep Convoloutional Networks for Large-Scale Image Recognition | Paper | Summary |

Amirreza Lashkari | 43 | Distributed Representations of Words and Phrases and their Compositionality | Paper | Summary |

Xinran Liu | 19 | Joint training of a convolutional network and a graphical model for human pose estimation | Paper | Summary |

Chris Choi | Learning Long-Range Vision for Autonomous Off-Road Driving | Paper | Summary | |

Luyao Ruan | Deep Learning of the tissue-regulated splicing code | Paper | Summary | |

Abdullah Rashwan | Deep Convolutional Neural Networks For LVCSR | paper | Summary | |

Mahmood Gohari | 37 | On using very large target vocabulary for neural machine translation | paper | Summary |

Valerie Platsko | Learning Convolutional Feature Hierarchies for Visual Recognition | Paper | Summary | |

Derek Latremouille | Learning fast approximations of sparse coding | Paper | Summary | |

Ri Wang | Continuous space language models | Paper | Summary |