Aaron Courville

Membre académique principal

courvila@mila.quebec

Chaire en IA Canada-CIFAR

Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle

Sujets de recherche

Apprentissage de représentations

Apprentissage par renforcement

Apprentissage profond

Communication efficace dans un jeu de somme générale

Modèles génératifs

Systèmes multi-agents

Théorie des jeux

Traitement du langage naturel

Vision par ordinateur

Biographie

Aaron Courville est professeur au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal et Directeur scientifique à IVADO. Il a obtenu son doctorat au Robotics Institute de l'Université Carnegie Mellon.

Il est l'un des premiers contributeurs à l'apprentissage profond, membre fondateur de Mila – Institut québécois d’intelligence artificielle. Avec Ian Goodfellow et Yoshua Bengio, il a coécrit le manuel de référence sur l'apprentissage profond.

Ses recherches actuelles portent sur le développement de modèles et de méthodes d'apprentissage profond. Il s'intéresse particulièrement à l'apprentissage par renforcement, à l'apprentissage par renforcement multi-agents, aux modèles génératifs profonds et au raisonnement.

Aaron Courville est titulaire d'une chaire en IA Canada-CIFAR et d'une Chaire de recherche du Canada (CRC) en généralisation systématique. Ses recherches ont été soutenues en partie par Microsoft Research, Samsung, Hitachi, Meta, Sony (bourse de recherche) et Google (bourse de recherche ciblée).

Étudiants actuels

Doctorat - UdeM

Alan Alan

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Laurent Charlin

Github

Juan Duque

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Uday Kapur

Doctorat - UdeM

Amr Khalifa

Doctorat - UdeM

Github

Samuel Lavoie

Doctorat - UdeM

Zhixuan Lin

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Johan Samir Obando Ceron

Doctorat - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Doctorat - UdeM

Maîtrise recherche - UdeM

Site web

Esra'a Saleh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Glen Berseth

Site web

Google Scholar

Vedant Shah

Doctorat - UdeM

Doctorat - UdeM

Yusong Wu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Anna (Cheng-Zhi) Huang

Sujin yun

Doctorat - UdeM

Doctorat - UdeM

Publications

Planning in Dynamic Environments with Conditional Autoregressive Models

Johanna Hansen

Kyle Kastner

Aaron Courville

Gregory Dudek

We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (voir plus)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.

2018-11-24

ArXiv (prépublication)

arxiv.org

Harmonic Recomposition using Conditional Autoregressive Modeling

We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(20… (voir plus)17). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.

2018-11-17

ArXiv (prépublication)

arxiv.org

Blindfold Baselines for Embodied QA

We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question b… (voir plus)y intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.

2018-11-11

ArXiv (prépublication)

doi.org

arxiv.org

Sim-to-Real Transfer with Neural-Augmented Robot Simulation

Despite the recent successes of deep reinforcement learning, teaching complex motor skills to a physical robot remains a hard problem. While… (voir plus) learning directly on a real system is usually impractical, doing so in simulation has proven to be fast and safe. Nevertheless, because of the "reality gap," policies trained in simulation often perform poorly when deployed on a real system. In this work, we introduce a method for training a recurrent neural network on the differences between simulated and real robot trajectories and then using this model to augment the simulator. This Neural-Augmented Simulation (NAS) can be used to learn control policies that transfer significantly better to real environments than policies learned on existing simulators. We demonstrate the potential of our approach through a set of experiments on the Mujoco simulator with added backlash and the Poppy Ergo Jr robot. NAS allows us to learn policies that are competitive with ones that would have been learned directly on the real robot.

2018-10-22

Proceedings of The 2nd Conference on Robot Learning (publié)

proceedings.mlr.press

Deep Learning. Das umfassende Handbuch

Ian Goodfellow

Yoshua Bengio

Aaron Courville

2018-10-09

(publié)

www.semanticscholar.org

Visual Reasoning with Multi-hop Feature Modulation

Florian Strub

Mathieu Seurin

Ethan Perez

Harm de Vries

Jérémie Mary

P. Preux

Aaron Courville

Olivier Pietquin

2018-10-05

Computer Vision – ECCV 2018 (publié)

doi.org

arxiv.org

Convergence Properties of Deep Neural Networks on Separable Data

Remi Tachet des Combes

Mohammad Pezeshki

Samira Shabanian

Aaron Courville

Yoshua Bengio

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely… (voir plus) misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features’ frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize gradient starvation where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.

2018-09-26

(publié)

openreview.net