# Difference between revisions of "f15Stat946PaperSignUp"

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[http://goo.gl/forms/RASFRZXoxJ Your feedback on presentations] | [http://goo.gl/forms/RASFRZXoxJ Your feedback on presentations] | ||

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=Set A= | =Set A= | ||

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|Oct 30 ||Amirreza Lashkari|| 21 ||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|| 21 ||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]] | ||

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|Nov 6 || Ali Ghodsi || || Lecturer|||| | |Nov 6 || Ali Ghodsi || || Lecturer|||| | ||

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|Nov 13|| Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]] | |Nov 13|| Mike Hynes || 12 ||Speech recognition with deep recurrent neural networks || [http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf Paper] || [[Graves et al., Speech recognition with deep recurrent neural networks|Summary]] | ||

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− | |Nov 13 || Tim Tse || || | + | |Nov 13 || Tim Tse || || Question Answering with Subgraph Embeddings || [http://arxiv.org/pdf/1406.3676v3.pdf Paper] || [[Question Answering with Subgraph Embeddings | Summary ]] |

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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] || [[Neural Machine Translation: Jointly Learning to Align and Translate|Summary]] | |Nov 13 || Maysum Panju || ||Neural machine translation by jointly learning to align and translate ||[http://arxiv.org/pdf/1409.0473v6.pdf Paper] || [[Neural Machine Translation: Jointly Learning to Align and Translate|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]] | ||

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− | |Nov 20 || Valerie Platsko || ||Natural language processing (almost) from scratch. ||[http://arxiv.org/pdf/1103.0398.pdf Paper]|| | + | |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]] | ||

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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] || [[Genetics | 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/ | + | |Nov 27 ||Mahmood Gohari || ||Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships ||[http://pubs.acs.org/doi/pdf/10.1021/ci500747n paper]|| [[Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships|Summary]] |

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− | |Nov 27 || Derek Latremouille || || | + | |Nov 27 || Derek Latremouille || ||Learning Fast Approximations of Sparse Coding || [http://yann.lecun.com/exdb/publis/pdf/gregor-icml-10.pdf Paper] ||[[Learning Fast Approximations of Sparse Coding|Summary]] |

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|Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]] | |Nov 27 ||Xinran Liu || ||ImageNet Classification with Deep Convolutional Neural Networks ||[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Paper]||[[ImageNet Classification with Deep Convolutional Neural Networks|Summary]] | ||

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− | | | + | |TBA ||Ali Sarhadi|| ||Strategies for Training Large Scale Neural Network Language Models|| [http://www.msr-waypoint.com/pubs/175561/ASRU-2011.pdf Paper]||[[Strategies for Training Large Scale Neural Network Language Models|Summary]] |

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+ | |Nov 27 || Peter Blouw|| ||Memory Networks.|| [http://arxiv.org/pdf/1410.3916v10.pdf Paper]|| [[Memory Networks|Summary]] | ||

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|Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]] | |Dec 4 || Chris Choi || || On the difficulty of training recurrent neural networks || [http://www.jmlr.org/proceedings/papers/v28/pascanu13.pdf Paper] || [[On the difficulty of training recurrent neural networks | Summary]] | ||

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− | |Dec 4 || Fatemeh Karimi || ||MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION||[http://arxiv.org/pdf/1412.7755v2.pdf Paper]|| | + | |Dec 4 || Fatemeh Karimi || ||MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION||[http://arxiv.org/pdf/1412.7755v2.pdf Paper]||[[MULTIPLE OBJECT RECOGNITION WITH VISUAL ATTENTION | Summary]] |

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− | |Dec 4 || Jan Gosmann || || | + | |Dec 4 || Jan Gosmann || || On the Number of Linear Regions of Deep Neural Networks || [http://arxiv.org/pdf/1402.1869v2.pdf Paper] || [[On the Number of Linear Regions of Deep Neural Networks | Summary]] |

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− | |Dec 4 || Dylan Drover || || | + | |Dec 4 || Dylan Drover || 54 || Semi-supervised Learning with Deep Generative Models || [http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf Paper] || [[Semi-supervised Learning with Deep Generative Models | Summary]] |

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|width="30pt"|Link to the summary | |width="30pt"|Link to the summary | ||

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− | |Anthony Caterini || | + | |Anthony Caterini ||1 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]|| [[The Manifold Tangent Classifier|Summary]] |

+ | |- | ||

+ | |Jan Gosmann ||2 || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] || [[Neural Turing Machines|Summary]] | ||

+ | |- | ||

+ | |Brent Komer ||3 || Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers || [http://arxiv.org/pdf/1202.2160v2.pdf Paper] || [[Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines|Summary]] | ||

+ | |- | ||

+ | |Sean Aubin ||4 || Deep Sparse Rectifier Neural Networks || [http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf Paper] || [[Deep Sparse Rectifier Neural Networks|Summary]] | ||

+ | |- | ||

+ | |Peter Blouw||5 || Generating text with recurrent neural networks || [http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf Paper] || [[Generating text with recurrent neural networks|Summary]] | ||

+ | |- | ||

+ | |Tim Tse||6 || From Machine Learning to Machine Reasoning || [http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper] || [[From Machine Learning to Machine Reasoning | Summary ]] | ||

+ | |- | ||

+ | |Rui Qiao|| 7 || Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation || [http://arxiv.org/pdf/1406.1078v3.pdf Paper] || [[Learning Phrase Representations|Summary]] | ||

+ | |- | ||

+ | |Ftemeh Karimi|| 8 || Very Deep Convoloutional Networks for Large-Scale Image Recognition || [http://arxiv.org/pdf/1409.1556.pdf Paper] || [[Very Deep Convoloutional Networks for Large-Scale Image Recognition|Summary]] | ||

+ | |- | ||

+ | |Amirreza Lashkari|| 9 || Distributed Representations of Words and Phrases and their Compositionality || [http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf Paper] || [[Distributed Representations of Words and Phrases and their Compositionality|Summary]] | ||

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− | | | + | |Xinran Liu|| 10 || Joint training of a convolutional network and a graphical model for human pose estimation || [http://papers.nips.cc/paper/5573-joint-training-of-a-convolutional-network-and-a-graphical-model-for-human-pose-estimation.pdf Paper] || [[Joint training of a convolutional network and a graphical model for human pose estimation|Summary]] |

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− | | | + | |Chris Choi|| 11 || Learning Long-Range Vision for Autonomous Off-Road Driving || [http://yann.lecun.com/exdb/publis/pdf/hadsell-jfr-09.pdf Paper] || [[Learning Long-Range Vision for Autonomous Off-Road Driving|Summary]] |

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− | | | + | |Luyao Ruan|| 12 || Deep Learning of the tissue-regulated splicing code || [http://bioinformatics.oxfordjournals.org/content/30/12/i121.full.pdf+html Paper] || [[Deep Learning of the tissue-regulated splicing code| Summary]] |

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− | | | + | |Abdullah Rashwan|| 13 || Deep Convolutional Neural Networks For LVCSR || [http://www.cs.toronto.edu/~asamir/papers/icassp13_cnn.pdf paper] || [[Deep Convolutional Neural Networks For LVCSR| Summary]] |

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− | | | + | |Mahmood Gohari|| 14 || On using very large target vocabulary for neural machine translation || [http://arxiv.org/pdf/1412.2007v2.pdf paper] || [[On using very large target vocabulary for neural machine translation| Summary]] |

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− | | | + | |Valerie Platsko|| 15 || Learning Convolutional Feature Hierarchies for Visual Recognition || [http://papers.nips.cc/paper/4133-learning-convolutional-feature-hierarchies-for-visual-recognition Paper] || [[Learning Convolutional Feature Hierarchies for Visual Recognition | Summary]] |

|- | |- | ||

− | | | + | |Derek Latremouille|| 16 || The Wake-Sleep Algorithm for Unsupervised Neural Networks || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] || [[The Wake-Sleep Algorithm for Unsupervised Neural Networks | Summary]] |

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− | | | + | |Ri Wang|| 17 || Continuous space language models || [https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/ListenSemester2_2009_10/sdarticle.pdf Paper] || [[Continuous space language models | Summary]] |

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− | | | + | |Deepak Rishi|| 18 || Extracting and Composing Robust Features with Denoising Autoencoders || [http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf Paper] || [[Extracting and Composing Robust Features with Denoising Autoencoders | Summary]] |

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− | | | + | |Maysum Panju|| 19 || A fast learning algorithm for deep belief nets || [https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf Paper] || [[A fast learning algorithm for deep belief nets | Summary]] |

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− | | | + | |Michael Hynes|| 20 || The loss surfaces of multilayer networks || [http://arxiv.org/abs/1412.0233 Paper] || [[The loss surfaces of multilayer networks (Choromanska et al.) | Summary]] |

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− | | | + | |Dylan Drover|| 21 || Deep Generative Stochastic Networks Trainable by Backprop || [http://jmlr.org/proceedings/papers/v32/bengio14.pdf Paper] || [[Deep Generative Stochastic Networks Trainable by Backprop| Summary]] |

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− | | | + | |Ankit Pat|| 22 || Deep Boltzmann Machines || [http://www.utstat.toronto.edu/~rsalakhu/papers/dbm.pdf Paper] || [[Deep Boltzmann Machines| Summary]] |

## Latest revision as of 12:01, 16 October 2018

# 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 |

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 | Summary | |

Nov 27 | Derek Latremouille | Learning Fast Approximations of Sparse Coding | Paper | Summary | |

Nov 27 | Xinran Liu | ImageNet Classification with Deep Convolutional Neural Networks | Paper | Summary | |

TBA | Ali Sarhadi | Strategies for Training Large Scale Neural Network Language Models | Paper | Summary | |

Nov 27 | Peter Blouw | Memory Networks. | Paper | Summary | |

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 | Summary | |

Dec 4 | Jan Gosmann | On the Number of Linear Regions of Deep Neural Networks | Paper | Summary | |

Dec 4 | Dylan Drover | 54 | Semi-supervised Learning with Deep Generative Models | Paper | Summary |

# Set B

Name | Paper number | Title | Link to the paper | Link to the summary |

Anthony Caterini | 1 | The Manifold Tangent Classifier | Paper | Summary |

Jan Gosmann | 2 | Neural Turing machines | Paper | Summary |

Brent Komer | 3 | Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers | Paper | Summary |

Sean Aubin | 4 | Deep Sparse Rectifier Neural Networks | Paper | Summary |

Peter Blouw | 5 | Generating text with recurrent neural networks | Paper | Summary |

Tim Tse | 6 | From Machine Learning to Machine Reasoning | Paper | Summary |

Rui Qiao | 7 | Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation | Paper | Summary |

Ftemeh Karimi | 8 | Very Deep Convoloutional Networks for Large-Scale Image Recognition | Paper | Summary |

Amirreza Lashkari | 9 | Distributed Representations of Words and Phrases and their Compositionality | Paper | Summary |

Xinran Liu | 10 | Joint training of a convolutional network and a graphical model for human pose estimation | Paper | Summary |

Chris Choi | 11 | Learning Long-Range Vision for Autonomous Off-Road Driving | Paper | Summary |

Luyao Ruan | 12 | Deep Learning of the tissue-regulated splicing code | Paper | Summary |

Abdullah Rashwan | 13 | Deep Convolutional Neural Networks For LVCSR | paper | Summary |

Mahmood Gohari | 14 | On using very large target vocabulary for neural machine translation | paper | Summary |

Valerie Platsko | 15 | Learning Convolutional Feature Hierarchies for Visual Recognition | Paper | Summary |

Derek Latremouille | 16 | The Wake-Sleep Algorithm for Unsupervised Neural Networks | Paper | Summary |

Ri Wang | 17 | Continuous space language models | Paper | Summary |

Deepak Rishi | 18 | Extracting and Composing Robust Features with Denoising Autoencoders | Paper | Summary |

Maysum Panju | 19 | A fast learning algorithm for deep belief nets | Paper | Summary |

Michael Hynes | 20 | The loss surfaces of multilayer networks | Paper | Summary |

Dylan Drover | 21 | Deep Generative Stochastic Networks Trainable by Backprop | Paper | Summary |

Ankit Pat | 22 | Deep Boltzmann Machines | Paper | Summary |