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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		Learning Fast Approximations of Sparse Coding	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	Summary
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	Summary
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		The Wake-Sleep Algorithm for Unsupervised Neural Networks	Paper	Summary
Ri Wang		Continuous space language models	Paper	Summary
Deepak Rishi		Extracting and Composing Robust Features with Denoising Autoencoders	Paper	Summary
Maysum Panju		A fast learning algorithm for deep belief nets	Paper	Summary	Dylan Drover		Deep Generative Stochastic Networks Trainable by Backprop	Paper	Summary