Schedule
Lectures are in room 1140 (ground floor) of Pavillon Aisenstadt (2920 chemin de la tour).
First day of lectures: Monday August 3
Last day of lectures: Wednesday August 12
There will be lectures during the week-end, i.e., a total of 10 days of lectures.
The planned daily schedule is as follows:
8:30-9:00 refreshments
9:00-10:30 lecture 1
10:30-11:00 coffee break
11:00-12:30 lecture 2
12:30-2:00 lunch break
2:00-3:30 lecture 3
3:30-4:00 coffee break
4:00-5:30 posters, deep learning programming tutorials, exercises, free time
Day 1, Mon, Aug 3
Pascal Vincent: Intro to ML (9:00-10:30)
Yoshua Bengio: Theoretical motivations for Representation Learning & Deep Learning (11:00-12:30)
Leon Bottou: Intro to multi-layer nets (2:00-3:30)
Posters Aug 3 (4:00-5:30):
Guillaume Alain (Université de Montréal): “Distributed Dropout in Asynchronous Stochastic Gradient Descent”
Amjad Almahairi (Université de Montréal): “Dynamic Knowledge Distillation”
Christof Angermueller (University of Cambridge): “Multi-task Deep Neural Networks for predicting CpG methylation from high-dimensional biological data”
Gunes Baydin (National University of Ireland Maynooth): “DiffSharp: Automatic Differentiation Library”
Lucas Beyer (RWTH Aachen University): “Torch7 + Theano = Beacon8”
Parminder Bhatia (Georgia Tech): “Sentiment Analysis of Short Text - Sentence & User Representation Learning”
Yuri Burda (University of Toronto): “Improved training of variational autoencoders”
Heeyoul Choi (Samsung Advanced Institute of Technology): “Improvements on LSTM”
Pradeep Dasigi (Carnegie Mellon University): “Ontologically grounded selectional preference autoencoders for modeling events”
Emily Denton (New York University): “Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks”
Michael Eickenberg (Université de Paris-Sud): “Convolutional Nets Map the Architecture of the Human Visual System”
Pablo Fonseca (University of Campinas): “A CNN approach to Breast Density Classification”
Mercedes Garcia (Université du Maine): “Class-based Continuous Space Language Model”
Ross Goroshin (New York University): “Learning to Linearize under Uncertainty”
Day 2, Tue, Aug 4
Hugo Larochelle: Neural nets and backprop (9:00-10:30)
Leon Bottou: Numerical optimization and SGD, Structured problems & reasoning (11:00-12:30)
Hugo Larochelle: Directed Graphical Models and NADE (2:00-3:30)
Intro to Theano (4:00-5:30)
Day 3, Wed, Aug 5
Aaron Courville: Intro to undirected graphical models (9:00-10:30)
Honglak Lee: Stacks of RBMs (11:00-12:30)
Pascal Vincent: Denoising and contractive auto-encoders, manifold view (2:00-3:30)
GPU Programming (4:00-5:30)
Day 4, Th, Aug 6
Roland Memisevic: Visual features (9:00-10:30)
Honglak Lee: Convolutional networks (11:00-12:30)
Graham Taylor: Learning similarity (2:00-3:30)
Posters Aug 6 (4:00-5:30):
Shixiang Gu (University of Cambridge): “Neural Adaptive Sequential Monte Carlo”
Philip Haeusser (Technische Universität München): “FlowNet: Learning Optical Flow with Convolutional Networks”
Anna Huang (Harvard University): “ChordRipple: Radical Chord Recommendations with chord2vec”
Jiwoong Im (Université de Montréal): “Conservativeness of untied auto-encoder”
Vamsi Ithapu (University of Wisconsin Madison): "Regularizing Deep networks via Matrix Completion”
Rico Jonschkowski (Technische Universität): “Learning State Representations with Robotic Priors”
Mikael Kageback (Chalmers University of Technology): “Neural context embeddings for automatic discovery of word senses”
Kazuya Kawakami (Carnegie Mellon University): “Learning to Representing Words in Context from Parallel Data for Improved Supersense
Tagging”
Suyoun Kim (Carnegie Mellon University): “Environmental Noise Embeddings for Robust Speech Recognition”
Kishore Reddy Konda (Goethe University Frankfurt): “Dropout as data augmentation”
Iryna Korshunova (Ghent University): “Epileptic Seizure Prediction using Convolutional Neural Networks”
David Krueger (Université de Montréal): “Speech Synthesis with Conditional Grammar Cells”
Angeliki Lazaridou (University of Trento): “Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation”
Kisuk Lee (Massachusetts Institute of Technology): “Recursive 2D-3D Convolutional Networks for Neuronal Boundary Detection”
Conrado Miranda (University of Campinas): “Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks”
Day 5, Fri, Aug 7
Chris Manning: NLP 101 (9:00-10:30)
Graham Taylor: Modeling human motion, pose estimation and tracking (11:00-12:30)
Chris Manning: NLP / Deep Learning (2:00-3:30)
Theano programming, datasets with Fuel (4:00-5:30)
Day 6, Sat, Aug 8
Ruslan Salakhutdinov: Deep Boltzmann Machines (9:00-10:30)
Adam Coates: Speech recognition with deep learning (11:00-12:30)
Ruslan Salakhutdinov: Multi-modal models (2:00-3:30)
Posters Aug 8 (4:00-5:30):
Olof Mogren (Chalmers University of Technology): “Extractive Summarization by Aggregating Multiple Similarities”
Tsendsuren Munkhdalai (Chungbuk National University): “DeepText: End-to-end biomedical event extraction via deep learning and recursive projection model”
Natalia Neverova (INSA-Lyon / University of Guelph): “Hand pose estimation by deep transductive learning”
Minh Nguyen (Rice University): “A Probabilistic Theory of Deep Learning”
Makoto Otsuka (Castalia Co., Ltd.): “Dynamic Boltzmann machines with homeostatic spike-timing dependent plasticity”
Lionel Pigou (Ghent University): “Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video”
Dan Rosenbaum (The Hebrew University): “The Return of the Gating Network: generative models and discriminative training in image priors”
Levent Sagun (New York University): “Explorations on high dimensional landscapes”
Joao Felipe Santos (Institut National de la Recherche Scientifique): “Using convolutional and recurrent layers in deep neural networks for spectral speech enhancement”
Irina Sergienya (University of Munich): “Learning Better Embeddings for Rare Words Using Distributional Representations”
Soeren Kaae Soenderby (University of Copenhagen): “Transformer Networks for Classifying Sequences”
Ruben Villegas (University of Michigan): “Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured”
Weizhong Yan (GE Global Research Center): “Deep Learning for Industrial Asset Condition Monitoring”
Wenpeng Yin (University of Munich): “Learning Word Meta-Embeddings by Combining Diverse Embedding Versions”
Asja Fischer (Université de Montréal): “Difference Target-Propagation”
Day 7, Sun, Aug 9
Ian Goodfellow: Structure of optimization problems (9:00-10:30)
Adam Coates: Systems issues and distributed training (11:00-12:30)
Ian Goodfellow: Adversarial examples (2:00-3:30)
Day 8, Mon, Aug 10
Phil Blunsom: From language modeling to machine translation (9:00-10:30)
Richard Socher: Recurrent neural networks (11:00-12:30)
Phil Blunsom: Memory, Reading, and Comprehension (2:00-3:30)
Theano debugging tools, conv. nets (4:00-5:30)
Day 9, Tue, Aug 11
Richard Socher: DMN for NLP (9:00-10:30)
Mark Schmidt: Smooth, Finite, and Convex Optimization (11:00-12:30)
Roland Memisevic: Visual Features II (2:00-3:30)
Theano implementation of iterations (scan), RNNs (4:00-5:30)
Day 10, Wed, Aug 12
Mark Schmidt: Non-Smooth, Non-Finite, and Non-Convex Optimization (9:00-10:30)
Aaron Courville: VAEs and deep generative models for vision (11:00-12:30)
Yoshua Bengio: Generative models from auto-encoders (2:00-3:30)
Theano programming: overflow session (4:00-5:00)
Farewell party (5:00-) registration form