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[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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|Mkeup Class (TBA) || Peter Blouw|| ||Memory Networks.|| [http://arxiv.org/abs/1410.3916]|| [[Memory 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 || || . From machine learning to machine reasoning. Mach. Learn. ||[http://research.microsoft.com/pubs/206768/mlj-2013.pdf Paper]||
|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] ||
|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/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/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 || ||The Wake-Sleep Algorithm for Unsupervised Neural Networks  || [http://www.gatsby.ucl.ac.uk/~dayan/papers/hdfn95.pdf Paper] ||
|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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|Nov 27 ||Ali Sarhadi|| ||Strategies for Training Large Scale Neural Network Language Models||||
|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 || ||  A fast learning algorithm for deep belief nets || [http://www.mitpressjournals.org/doi/pdf/10.1162/neco.2006.18.7.1527 Paper] || [[A fast learning algorithm for deep belief nets | Summary]]
|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 || || Towards AI-complete question answering: a set of prerequisite toy tasks || [http://arxiv.org/pdf/1502.05698.pdf Paper] ||
|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 ||15 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]||
|Anthony Caterini ||1 ||The Manifold Tangent Classifier ||[http://papers.nips.cc/paper/4409-the-manifold-tangent-classifier.pdf Paper]|| [[The Manifold Tangent Classifier|Summary]]
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|Jan Gosmann ||2 || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] || [[Neural Turing Machines|Summary]]
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|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]]
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|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]]
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|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]]
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|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 ]]
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|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]]
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|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]]
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|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]]
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|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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|Jan Gosmann || || Neural Turing machines || [http://arxiv.org/abs/1410.5401 Paper] ||
|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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|Brent Komer || || Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers || [http://arxiv.org/pdf/1202.2160v2.pdf Paper] ||
|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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|Sean Aubin || || Deep Sparse Rectifier Neural Networks || [http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf Paper] || [[Deep Sparse Rectifier Neural Networks|Summary]]
|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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|Peter Blouw|| || Generating text with recurrent neural networks || [http://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf Paper] ||
|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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|Tim Tse|| || Question answering with subgraph embeddings || [http://arxiv.org/pdf/1406.3676v3.pdf Paper] ||
|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
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