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

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

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

Representation Mixing for TTS Synthesis

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. Ho… (voir plus)wever, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.

2019-05-11

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

doi.org

arxiv.org

Brief Report: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Yikeng Shen

Shawn Tan

Alessandro Sordoni

Aaron Courville

2019-05-05

(publié)

www.semanticscholar.org

Counterpoint by Convolution

Cheng-Zhi Anna Huang

Tim Cooijmans

Adam Roberts

Aaron Courville

Douglas Eck

Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a … (voir plus)single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling. We demonstrate the versatility of our method on unconditioned polyphonic music generation.

2019-03-17

ArXiv (prépublication)

openreview.net

Maximum Entropy Generators for Energy-Based Models

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In t… (voir plus)his work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient. The resulting objective requires maximizing entropy of the generated samples, which we perform using recently proposed nonparametric mutual information estimators. Finally, to stabilize the resulting adversarial game, we use a zero-centered gradient penalty derived as a necessary condition from the score matching literature. The proposed technique can generate sharp images with Inception and FID scores competitive with recent GAN techniques, does not suffer from mode collapse, and is competitive with state-of-the-art anomaly detection techniques.

2019-01-23

ArXiv (prépublication)

doi.org

arxiv.org

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Kundan Kumar

Rithesh Kumar

Thibault de Boissiere

Lucas Gestin

Wei Zhen Teoh

Jose Sotelo

Alexandre De Brébisson

Yoshua Bengio

Aaron Courville

2018-12-31

Neural Information Processing Systems (publié)

doi.org

arxiv.org

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger c… (voir plus)onstituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

2018-12-31

ICLR.cc/2019/Conference (présentation orale)

openreview.net

No Press Diplomacy: Modeling Multi-Agent Gameplay

Philip Paquette

Yuchen Lu

Steven Bocco

Max O. Smith

Satya Ortiz-Gagné

Jonathan K. Kummerfeld

Satinder Singh

Joelle Pineau

Aaron Courville

Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Rel… (voir plus)iance on trust and coordination makes Diplomacy the first non-cooperative multi-agent benchmark for complex sequential social dilemmas in a rich environment. In this work, we focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players. We present DipNet, a neural-network-based policy model for No Press Diplomacy. The model was trained on a new dataset of more than 150,000 human games. Our model is trained by supervised learning (SL) from expert trajectories, which is then used to initialize a reinforcement learning (RL) agent trained through self-play. Both the SL and RL agents demonstrate state-of-the-art No Press performance by beating popular rule-based bots.

2018-12-31

Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (publié)

doi.org

arxiv.org

Probability Distillation: A Caveat and Alternatives

Alexandre Lacoste

Due to Van den Oord et al. (2018), probability distillation has recently been of interest to deep learning practitioners, where, as a practi… (voir plus)cal workaround for deploying autoregressive models in real-time applications, a student network is used to obtain quality samples in parallel. We identify a pathological optimization issue with the adopted stochastic minimization of the reverse-KL divergence: the curse of dimensionality results in a skewed gradient distribution that renders training inefﬁcient. This means that KL-based “evaluative” training can be susceptible to poor exploration if the target distribution is highly structured. We then explore alternative principles for distillation, including one with an “instructive” signal, and show that it is possible to achieve qualitatively better results than with KL minimization.

2018-12-31

Conference on Uncertainty in Artificial Intelligence (publié)

proceedings.mlr.press

Systematic Generalization: What Is Required and Can It Be Learned?

Dzmitry Bahdanau*

Shikhar Murty*

2018-12-31

ICLR.cc/2019/Conference (poster)

openreview.net

On the Spectral Bias of Neural Networks

Nasim Rahaman

Felix Draxler

Fred A. Hamprecht

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with …

2018-12-31

International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

Towards Jumpy Planning

Akilesh

Suriya Singh

Anirudh Goyal

Alexander Neitz

Aaron Courville

Model-free reinforcement learning (RL) is a powerful paradigm for learning complex tasks but suffers from high sample inefficiency as well a… (voir plus)s ignorance of the environment dynamics. On the other hand, a model-based RL agent learns dynamical causal models of the environment and uses them to plan. However, using a model at the scale of time-steps (usually tens of milliseconds) is mostly unfeasible in practice due to compounding prediction errors and computational requirements for making vast numbers of model queries during the planning process. We propose to use a modelbased planner together with a goal-conditioned policy trained with model-free learning. We use a model-based planner that operates at higher levels of abstraction i.e., decision states and use modelfree RL between the decision states. We validate our approach in terms of transfer and generalization performance and show that it leads to improvement over model-based planner that jumps to states that are fixed timesteps ahead.

2018-12-31

(publié)

www.semanticscholar.org

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Cătălina Cangea

Eugene Belilovsky

Pietro Lio

Aaron Courville

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely o… (voir plus)n its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.

2018-12-31

ViGIL@NeurIPS (publié)

doi.org

arxiv.org

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Aaron Courville

Biographie

Étudiants actuels

Publications

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Mots-clés populaires:

Aaron Courville

Biographie

Étudiants actuels

Publications