Deep Learning Summer School, Montreal 2016

About

Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.

The Deep Learning Summer School 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

Videos

Invited Talks

55:36

Introduction to Torch

Alex Wiltschko

Aug 23, 2016

5625 views

Deep Learning Summer School, Montreal 2016

About

Videos

Invited Talks

Introduction to Torch

Alex Wiltschko

Learning Deep Generative Models

Ruslan Salakhutdinov

Beyond inspiration: Five lessons from biology on building intelligent machines

Bruno Olshausen

Introduction to Reinforcement Learning

Joelle Pineau

Recurrent Neural Networks

Yoshua Bengio

Beyond Seq2Seq with Augmented RNNs

Edward Grefenstette

Convolutional Neural Networks and Computer Vision

Rob Fergus

Learning to See

Antonio Torralba

Advanced Topics in RL

Joelle Pineau

Large Scale Deep Learning with TensorFlow

Jeffrey Dean

Machine Learning

Doina Precup

Reasoning, Attention and Memory

Sumit Chopra

Theoretical neuroscience and deep learning theory

Surya Ganguli

Introduction to Theano

Pascal Lamblin

Advanced Topics in RL

Doina Precup

Deep Natural Language Understanding

Kyunghyun Cho

Neural Networks

Hugo Larochelle

Building Machines that Imagine and Reason: Principles and Applications of Deep G...

Shakir Mohamed

Deep Reinforcement Learning

Pieter Abbeel

GPU programming for Deep Learning

Julie Bernauer,

Ryan Olson

Contributed Talks

Beam Search Message Passing in Bidirectional RNNs: Applications to Fill-in-the-B...

Qing Sun

Model-Based Relative Entropy Stochastic Search

Abbas Abdolmaleki

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

Tegan Maharaj

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Sina Honari

Analyzing the Behavior of Deep Visual Question Answering Models

Aishwarya Agrawal

Variational Autoencoders with PixelCNN Decoders

Ishaan Gulrajani

An Infinite Restricted Boltzmann Machine

Marc-Alexandre Côté

Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Ne...

Rajarshi Das

Professor Forcing: A New Algorithm for Training Recurrent Networks

Anirudh Goyal

Learning to Communicate with Deep Multi­-Agent Reinforcement Learning

Jakob Foerster

Learning Nash Equilibrium for General-Sum Markov Games from Batch Dat

Julien Pérolat

A Network-based End-to-End Trainable Task-oriented Dialogue System

Tsung-Hsien Wen

Deep siamese neural network for prediction of long-range interactions in chromat...

Davide Chicco

Inference Learning

Patrick Putzky

Deep multi-view representation learning of brain responses to natural stimuli

Leila Wehbe

Learning to Communicate with Deep Multi-Agent Reinforcement Learning