Difference between revisions of "f15Stat946PaperSignUp"

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(Set B)
(Set A)
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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]]
 
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|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]||
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|Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]|| [[Natural language processing (almost) from scratch. | Summary]]
 
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|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]
 
|Nov 20 || Brent Komer || ||Show, Attend and Tell: Neural Image Caption Generation with Visual Attention || [http://arxiv.org/pdf/1502.03044v2.pdf Paper]||[[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|Summary]]

Revision as of 10:34, 11 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
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 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 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 [2] Summary