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

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

Publications

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

David Krueger

Mohammad Pezeshki

Nan Rosemary Ke

Hugo Larochelle

Aaron Courville

Christopher Pal

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.

2015-12-31

arXiv (preprint)

Task Loss Estimation for Sequence Prediction

Dzmitry Bahdanau

Dmitriy Serdyuk

Philemon Brakel

Nan Rosemary Ke

Aaron Courville

2015-11-18

ArXiv (preprint)

Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks

Aaron Courville

Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output p… (see more)roblems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. In this paper we focus on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description, and speech recognition. All these systems are based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks, along with trained attention mechanisms. We report on experimental results with these systems, showing impressively good performance and the advantage of the attention mechanism.

2015-10-31

IEEE Transactions on Multimedia (published)

Generative Adversarial Nets

Moez Krichen

Ian J. Goodfellow

Jean Pouget-Abadie

Mehdi Mirza

David Warde-Farley

Aaron Courville

Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the acad… (see more)emia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

2014-06-09

Communications of the ACM (published)