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

Ayush Agrawal
PhD - Université de Montréal
Website
Github
Google Scholar
Reza Bayat
PhD - Université de Montréal
Co-supervisor :
Pascal Vincent
Website
Anirudh Buvanesh
PhD - Université de Montréal
Principal supervisor :
Laurent Charlin
anirudb1102@gmail.com
Github
Abhranil Chandra
Collaborating researcher - University of Waterloo
abhranil.chandra@gmail.com
Website
Github
Google Scholar
Razvan Ciuca
Master's Research - Université de Montréal
Github
Juan Duque
PhD - Université de Montréal
Website
Github
Google Scholar
Sarvjeet Ghotra
PhD - Université de Montréal
Github
Google Scholar
Arian Hosseini
PhD - Université de Montréal
Website
Github
Google Scholar
Amr Khalifa
PhD - Université de Montréal
Github
Samuel Lavoie
PhD - Université de Montréal
Zhixuan Lin
PhD - Université de Montréal
zxlin.cs@gmail.com
Website
Github
Google Scholar
Ahmed Masry
Collaborating researcher - N/A
Website
Github
Google Scholar
Alan Milligan
PhD - Université de Montréal
Principal supervisor :
Simon Lacoste-Julien
Website
Github
Google Scholar
Andjela Mladenovic
PhD - Université de Montréal
Principal supervisor :
Gauthier Gidel
Website
Google Scholar
Muqeeth Mohammed
PhD - Université de Montréal
Website
Github
Google Scholar
Morgane Moss
PhD - Université de Montréal
Co-supervisor :
Rishabh Agarwal
Andrei Nicolicioiu
PhD - Université de Montréal
andrei.nicolicioiu@gmail.com
Website
Google Scholar
Evgenii Nikishin
Collaborating Alumni - Université de Montréal
Principal supervisor :
Pierre-Luc Bacon
Website
Github
Google Scholar
Michael Noukhovitch
PhD - Université de Montréal
Website
Github
Google Scholar
Johan Samir Obando Ceron
PhD - Université de Montréal
Co-supervisor :
Pablo Samuel Castro
Website
Github
Google Scholar
Dereck Piché
Master's Research - Université de Montréal
pichedereck@gmail.com
Website
Github
Khaled Rouissi
Master's Research - Université de Montréal
Website
Esra'a Saleh
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Google Scholar
Vedant Shah
PhD - Université de Montréal
Website
Github
Google Scholar
Christos Tsirigotis
PhD - Université de Montréal
Github
Google Scholar
Yusong Wu
PhD - Université de Montréal
Principal supervisor :
Anna (Cheng-Zhi) Huang
sujin yun
PhD - Université de Montréal
Website
Google Scholar
Xiaofeng Zhang
PhD - Université de Montréal
Website
Dinghuai Zhang
PhD - Université de Montréal
Co-supervisor :
Yoshua Bengio
Website
Github
Hattie Zhou
PhD - Université de Montréal
Principal supervisor :
Hugo Larochelle

Publications

Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Guillaume Alain
Amjad Almahairi
Christof Angermüller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
Yoshua Bengio
Arnaud Bergeron
James Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (see 92 more)
Pierre Luc Carrier
Kyunghyun Cho
Jan Chorowski
Paul F. Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
M'elanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian G Goodfellow
Matthew Graham
Caglar Gulcehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
Sébastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric Larsen
César Laurent
S. Lee
Simon-mark Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
R. McGibbon
Roland Memisevic
Bart van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Chris Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Adriana Romero Soriano
Markus Dr. Roth
Peter Sadowski
John Salvatier
François Savard
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Subramanyam
Jakub Sygnowski
Jérémie Tanguay
Gijs van Tulder
Joseph Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
M. Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2016-05-09
ArXiv (preprint)
arxiv.org
arxiv.org
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Iulian V. Serban
Alessandro Sordoni
Ryan Lowe
Laurent Charlin
Joelle Pineau
Aaron Courville
Yoshua Bengio
Sequential data often possesses hierarchical structures with complex dependencies between sub-sequences, such as found between the utterance… (see more)s in a dialogue. To model these dependencies in a generative framework, we propose a neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with other recent neural-network architectures. We evaluate the model performance through a human evaluation study. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate both the generation of meaningful, long and diverse responses and maintaining dialogue state.
2016-05-01
ArXiv (preprint)
doi.org
arxiv.org
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Iulian V. Serban
Alessandro Sordoni
Yoshua Bengio
Aaron Courville
Joelle Pineau
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Ge… (see more)nerative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.
2016-03-05
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Task Loss Estimation for Structured Prediction
Dzmitry Bahdanau
D. Serdyuk
Philemon Brakel
Nan Rosemary Ke
Jan Chorowski
Aaron Courville
Yoshua Bengio
Former NASA chief unveils $ 100 million neural chip maker KnuEdge
C. Strasser
Dean Takahashi
Tim Klinger
Gerald Tesauro
Kartik Talamadupula
Bowen Zhou
Yoshua Bengio
Aaron Courville
Medium, Moore Data, Carly Strasser from June 07, 2016 Open access to research articles has been in the news quite a bit lately (see the SciH… (see more)ub controversy, the preprints in biology discussion, and the European Union’s recent announcement). The Data-Driven Discovery team at the Moore Foundation has also been discussing open access, particularly as it relates to the publications generated by our #MooreData researchers. Our grantee population is fairly progressive when it comes to open science, and many of the outputs that they generate are already publicly available (including proposals, software, workflows, and publications). It is therefore easy for us to imagine that they would embrace a policy that mandates open access for research articles that they produce. That said, we are always open to discussions!
Professor Forcing: A New Algorithm for Training Recurrent Networks
Anirudh Goyal
Alex Lamb
Ying Zhang
Saizheng Zhang
Aaron Courville
Yoshua Bengio
The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the networ… (see more)k’s own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.
arxiv.org
Generative adversarial networks
Moez Krichen
Ian G Goodfellow
Jean Pouget-Abadie
Mehdi Mirza
Bing Xu
David Warde-Farley
Sherjil Ozair
Aaron Courville
Yoshua Bengio
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal … (see more)of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.
2014-06-10
Communications of the ACM (published)
doi.org
arxiv.org