Portrait of Aaron Courville

Aaron Courville

Core Academic Member

courvila@mila.quebec

Canada CIFAR AI Chair

Full Professor, Université de Montréal, Department of Computer Science and Operations Research

Research Topics

Computer Vision

Deep Learning

Efficient Communication in General Sum Game

Game Theory

Generative Models

Multi-Agent Systems

Natural Language Processing

Reinforcement Learning

Representation Learning

Biography

Aaron Courville is a professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and Scientific Director of IVADO. He has a PhD from the Robotics Institute, Carnegie Mellon University.

Courville was an early contributor to deep learning: he is a founding member of Mila – Quebec Artificial Intelligence Institute. Together with Ian Goodfellow and Yoshua Bengio, he co-wrote the seminal textbook on deep learning.

His current research focuses on the development of deep learning models and methods. He is particularly interested in reinforcement learning, multi-agent reinforcement learning, deep generative models and reasoning.

Courville holds a Canada CIFAR AI Chair and a Canada Research Chair in Systematic Generalization. His research has been supported by Microsoft Research, Samsung, Hitachi, Meta, Sony (Research Award) and Google (Focused Research Award).

Current Students

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Anirudh Buvanesh

PhD - Université de Montréal

Principal supervisor :

Laurent Charlin

Master's Research - Université de Montréal

PhD - Université de Montréal

Sarvjeet Ghotra

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

Andjela Mladenovic

PhD - Université de Montréal

Principal supervisor :

Muqeeth Mohammed

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

Andrei Nicolicioiu

PhD - Université de Montréal

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

Michell Payano Perez

Collaborating researcher - Université de Montréal

Master's Research - Université de Montréal

Master's Research - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Christos Tsirigotis

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Publications

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

David Scott Krueger

J'anos Kram'ar

Mohammad Pezeshki

Nan Rosemary Ke

Hugo Larochelle

Aaron Courville

We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (see more)r previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find that zoneout gives significant performance improvements across tasks. We achieve competitive results with relatively simple models in character- and word-level language modelling on the Penn Treebank and Text8 datasets, and combining with recurrent batch normalization yields state-of-the-art results on permuted sequential MNIST.

2016-06-03

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (see more)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (see more)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (see more)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (see more)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)

Movie Description

Anna Rohrbach

Marcus Rohrbach

Niket Tandon

Hugo Larochelle

Aaron Courville

Bernt Schiele

2016-05-12

ArXiv (preprint)