Schedule

   01/08/2016   02/08/2016   03/08/2016   04/08/2016   05/08/2016   06/08/2016   07/08/2016 
9:00
10:30
Doina
Precup

Machine Learning
Rob
Fergus

Introduction
to CNNs
Yoshua
Bengio

RNNs
Kyunghyun
Cho

Natural
Language
Understanding
Joelle
Pineau

Reinforcement
Learning
Ruslan
Salakhutdinov

Deep Generative
Models I
Bruno
Olshausen

Comp.
Neuro I
10:30
11:00
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
11:00
12:30
Hugo Larochelle

Neural Networks I
Antonio Torralba

Learning
to See
Sumit
Chopra

Attention
and Memory
Edward Grefenstette

Beyond Seq2Seq
Pieter
Abbeel

Deep RL
Shakir
Mohamed

Building Machines that Imagine and Reason
Surya
Ganguli

Comp. Neuro II
and Deep Learning Theory
12:30
14:30
Lunch Lunch Lunch
WiDL event
Lunch Lunch Lunch Lunch
14:30
16:00
Hugo Larochelle

Neural Networks II 
Alex Wiltschko
Torch I
Jeff
Dean

Large Scale Deep Learning 

& TensorFlow 
Julie Bernauer
(NVIDIA)

GPU programming with CUDA
Joelle, Pieter  & Doina
Advanced Topics in RL
Contributed
talks


Session 4
Contributed
talks


Session 4
16:00
16:30
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
16:30
18:00
Pascal
Lamblin

Theano I
Practical
Session

 Alex Wiltschko
(Torch)

Frédéric Bastien
(Theano)
Jeff
Dean

Large Scale Deep Learning 

& TensorFlow
Contributed
talks


Session 1
Contributed
talks


Session 2
Contributed
Posters


Session 1
Contributed
Posters


Session 2
               
 Evening Opening Reception
(18:00-20:30)
-- by --
Imagia
   Happy Hour
(18:45-22:30)
buses at 18:30

-- by --
Maluuba

Happy Hour
(18:30-20:30)
--  by --
Creative Destruction Lab  
   

Contributed Talks

  • Session 1 (16:30-18:00, August 4th) 
    • Rajarshi Das: Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
    • Anirudh Goyal: Professor Forcing: A New Algorithm for Training Recurrent Networks
    • Tegan Maharaj: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
    • Leila Wehbe: Deep multi-view representation learning of brain responses to natural stimuli
  • Session 2 (16:30-18:00, August 5th) 
    • Jakob Foerster: Learning to Communicate with Deep Multi­-Agent Reinforcement Learning
    • Abbas Abdolmaleki: Model-Based Relative Entropy Stochastic Search
    • Julien Pérolat: Learning Nash Equilibrium for General-Sum Markov Games from Batch Data
    • Tsung-Hsien Wen: A Network-based End-to-End Trainable Task-oriented Dialogue System
  • Session 3 (14:30-16:00, August 6th) 
    • Patrick Putzky: Inference Learning
    • Ishaan Gulrajani: Variational Autoencoders with PixelCNN Decoders
    • Marc-Alexandre Côté: An Infinite Restricted Boltzmann Machine
    • Davide Chicco: Deep siamese neural network for prediction of long-range interactions in chromatin
  • Session 4 (14:30-16:00, August 7th) 
    • Qing Sun: Beam Search Message Passing in Bidirectional RNNs: Applications to Fill-in-the-Blank Image Captioning
    • Aishwarya Agrawal: Analyzing the Behavior of Deep Visual Question Answering Models
    • Christophe Zimmer: Faster super-resolution localization microscopy with deep learning
    • Sina Honari: Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Contributed Posters

  • Session 1 (16:30-18:00, August 6th) 
    • Alejandro Fernandez-Bujan: Degeneracy control in overcomplete ICA
    • Devon Hjelm: Iterative Refinement of the Approximate Posterior For Directed Belief Networks
    • Heechul Jung: Less-forgetting Learning in Deep Neural Networks
    • Pei-Hao Su: Deep Reinforcement Learning for Spoken Dialogue Management
    • Po-Yao Huang: Attention-based Multimodal Neural Machine Translation
    • Thomas Kipf: SEMIGCN: Semi-Supervised Node Classification with Graph Convolutional Neural Networks
    • David Vazquez: The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scene
    • Deborah Hanus: Inferring missing data & accounting for patient variation to predict effective HIV treatments
    • Zbigniew Wojna: Rethinking the Inception Architecture for Computer Vision
    • Fabio Maria Carlucci: When Naıve Bayes Nearest Neighbours Meet Convolutional Neural Networks
    • Melanie Ducoffe: Active learning strategy for CNN combining batchwise Dropout and Query-By-Committee
    • Kushal Kafle: Answer-Type Prediction for Visual Question Answering
    • Ryan Lowe: Automatic Evaluation of Dialogue Responses
    • Pavan Ramkumar: Understanding biological computation through synthetic neurophysiology
    • Frederik Ruelens: Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control
    • Luiza Sayfullina: Fixing random weight initialization as the form of regularization on the noisy datasets
    • Dmitry Ulyanov: Second-order residual networks
    • Sivanand Achanta: Elman Recurrent Neural Networks in Text-to-Speech Synthesis
    • Viktoriya Krakovna: Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
    • Federico Raue: Unsupervised Association using Multilayer Perceptron
    • Pyry Takala: DopeLearning: A Computational Approach to Rap Lyrics Generation
    • Victor Escorcia: DAPs: Deep Action Proposals for Action Understanding
    • Juan Camilo Gamboa Higuera: Bayesian Neural Networks for Model Based Reinforcement Learning in Robotics
    • Alan Bekker: Training deep neural-networks based on unreliable labels
    • Kenji Kawaguchi: Deep Learning without Poor Local Minima
    • Varun Kacholia: How Facebook News Feed uses Deep Nets
  • Session 2 (16:30-18:00, August 7th) 
      • Yen­-Chang Hsu: Neural Network-­based Clustering Using Pairwise Constraints
      • Andrew Jaegle: Learning to predict body shape from images
      • Michael Kampffmeyer: Semantic Segmentation of Small Objects and Uncertainty in Urban Remote Sensing Images Using CNNs
      • Ekaterina Kochmar: Detecting Learner Errors in the Choice of Content Words Using Compositional Distributional Semantics
      • Adrian Lancucki: Clone Wars: Improved Data Visualization with t-SNE
      • Alex Rubinsteyn: Predicting Peptide-MHC Binding Affinity With Imputed Training Data and Recurrent Neural Networks
      • Florian Shkurti: Texture-Aware SLAM Using Stereo Imagery And Inertial Information
      • Pedro Tabacof: Exploring the Space of Adversarial Images
      • Ramana Subramanyam: Foreground Detection in Images
      • Samira Ebrahimi Kahou: Recurrent Neural Networks for Emotion Recognition in Video
      • Vincent Michalski: RATM: Recurrent Attentive Tracking Model
      • Diogo Moitinho de Almeida: Deep Gated Networks in RNNs
      • Marielle Malfante: Automatic fish sounds detection and classification
      • Farnood Merrikh Bayat: Analog hardware implementation of neural networks using emerging nonvolatile memory technologies
      • Yash Goyal: Interpreting Visual Question Answering Models
      • Sarath Chandar: Correlational Neural Networks
      • Thomas Pellegrini: What does a CNN trained for phone recognition learn?
      • Ramprasaath Ramasamy Selvaraju: Grad-CAM: Gradient-weighted Class Activation Mapping
      • Florian Strub: Recommender Systems (Collaborative Filtering) with Neural Networks
      • Jie Fu: Deep Q-Networks for Accelerating the Training of Deep Neural Networks
      • Ludovic Trottier: Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
      • Ke Zhang: Video Summarization with Long Short-term Memory
      • Wei-Lun Chao: An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
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