Yoshua Bengio

Biographie

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Reconnu comme une sommité mondiale en intelligence artificielle, Yoshua Bengio s’est surtout distingué par son rôle de pionnier en apprentissage profond, ce qui lui a valu le prix A. M. Turing 2018, le « prix Nobel de l’informatique », avec Geoffrey Hinton et Yann LeCun. Il est professeur titulaire à l’Université de Montréal, fondateur et conseiller scientifique de Mila – Institut québécois d’intelligence artificielle, et codirige en tant que senior fellow le programme Apprentissage automatique, apprentissage biologique de l'Institut canadien de recherches avancées (CIFAR). Il occupe également la fonction de conseiller spécial et directeur scientifique fondateur d’IVADO.

En 2018, il a été l’informaticien qui a recueilli le plus grand nombre de nouvelles citations au monde. En 2019, il s’est vu décerner le prestigieux prix Killam. Depuis 2022, il détient le plus grand facteur d’impact (h-index) en informatique à l’échelle mondiale. Il est fellow de la Royal Society de Londres et de la Société royale du Canada, et officier de l’Ordre du Canada.

Soucieux des répercussions sociales de l’IA et de l’objectif que l’IA bénéficie à tous, il a contribué activement à la Déclaration de Montréal pour un développement responsable de l’intelligence artificielle.

Étudiants actuels

Jamal Abou Haibeh

Collaborateur·rice alumni - McGill

Mohammed Abukalam

Collaborateur·rice alumni - UdeM

Berkes Anaïs

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

Rim Assouel

Doctorat - UdeM

Junyeob BAEK

Visiteur de recherche indépendant - KAIST

Visiteur de recherche indépendant

Co-superviseur⋅e :

Guillaume Lajoie

Paul Bertin

Doctorat - UdeM

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Doctorat - UdeM

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Loubna Benabbou

Eric Elmoznino

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Leo Feng

Doctorat - UdeM

leo.feng@mila.quebec

Ivan Grega

Stagiaire de recherche - UdeM

Doctorat

Doctorat - UdeM

mohsin.hasan@mila.quebec

Edward Hu

Doctorat - UdeM

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - UdeM

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernandez

Yaroslav KIVVA

Collaborateur·rice de recherche - UdeM

Salem Lahlou

Collaborateur·rice alumni - UdeM

Tabitha Edith Lee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Seanie Lee

Collaborateur·rice alumni - UdeM

Collaborateur·rice alumni

Zhen Liu

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Kanika Madan

Doctorat - UdeM

Nikolay Malkin

Collaborateur·rice alumni - UdeM

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sören Mindermann

Collaborateur·rice de recherche - UdeM

Sarthak Mittal

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Visiteur de recherche indépendant - UdeM

Padideh Nouri

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Ali Parviz

Collaborateur·rice de recherche - Ying Wu Coll of Computing

Camille Rochefort-Boulanger

Lena Podina

Doctorat - University of Waterloo

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - Max-Planck-Institute for Intelligent Systems

Amine RAZIG

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Postdoctorat - UdeM

Visiteur de recherche indépendant - UdeM

Postdoctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

Victor Schmidt

Collaborateur·rice alumni - UdeM

Postdoctorat - UdeM

Maîtrise recherche - UdeM

Marcin Sendera

Collaborateur·rice alumni - UdeM

Vedant Shah

Maîtrise recherche - UdeM

Postdoctorat

Marco Stock

Visiteur de recherche indépendant - Technical University of Munich

marco.stock@tum.de

Mélisande Astrid Crystal Teng

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

alexander.tong@mila.quebec

Alex Tong

Postdoctorat - UdeM

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Omar G. Younis

Collaborateur·rice de recherche

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Aaron Courville

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

Harry Zhao

Doctorat - McGill

Superviseur⋅e principal⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Mise à l’échelle au service du raisonnement et de l’apprentissage automatique basé sur un modèle

Scaling in the service of reasoning & model-based ML

4 avril 2023

par

Yoshua Bengio

Edward J. Hu

Une collaboration entre Mila et Relation Therapeutics pour découvrir in vitro de nouvelles associations médicamenteuses synergiques

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

23 mars 2022

par

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

Les réseaux de flot génératifs

15 mars 2022

par

Yoshua Bengio

Publications

COVI White Paper

Hannah Alsdurf

Tristan Deleu

Prateek Gupta

Daphne Ippolito

Richard Janda

Max Jarvie

Tyler J. Kolody

Sekoul Krastev

Tegan Maharaj

Robert Obryk

Dan Pilat

Valerie Pisano

Benjamin Prud'homme

Meng Qu

Nasim Rahaman

Irina Rish

Jean-franois Rousseau

Abhinav Sharma

Brooke Struck … (voir 3 de plus)

Jian Tang

Martin Weiss

Yun William Yu

2020-05-18

ArXiv (prépublication)

Multi-Task Self-Supervised Learning for Robust Speech Recognition

Mirco Ravanelli

Jianyuan Zhong

Santiago Pascual

Pawel Swietojanski

Joao Monteiro

Jan Trmal

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To t… (voir plus)ake a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation.Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions.

2020-05-04

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation

Maxence Ernoult

Julie Grollier

Damien Querlioz

Benjamin Scellier

Equilibrium Propagation (EP) is a biologically inspired alternative algorithm to backpropagation (BP) for training neural networks. It appli… (voir plus)es to RNNs fed by a static input x that settle to a steady state, such as Hopfield networks. EP is similar to BP in that in the second phase of training, an error signal propagates backwards in the layers of the network, but contrary to BP, the learning rule of EP is spatially local. Nonetheless, EP suffers from two major limitations. On the one hand, due to its formulation in terms of real-time dynamics, EP entails long simulation times, which limits its applicability to practical tasks. On the other hand, the biological plausibility of EP is limited by the fact that its learning rule is not local in time: the synapse update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically. Our work addresses these two issues and aims at widening the spectrum of EP from standard machine learning models to more bio-realistic neural networks. First, we propose a discrete-time formulation of EP which enables to simplify equations, speed up training and extend EP to CNNs. Our CNN model achieves the best performance ever reported on MNIST with EP. Using the same discrete-time formulation, we introduce Continual Equilibrium Propagation (C-EP): the weights of the network are adjusted continually in the second phase of training using local information in space and time. We show that in the limit of slow changes of synaptic strengths and small nudging, C-EP is equivalent to BPTT (Theorem 1). We numerically demonstrate Theorem 1 and C-EP training on MNIST and generalize it to the bio-realistic situation of a neural network with asymmetric connections between neurons.

2020-04-29

ArXiv (prépublication)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Miles Brundage

Shahar Avin

Jasmine Wang

Haydn Belfield

Gretchen Krueger

Gillian K. Hadfield

Heidy Khlaaf

Jingying Yang

H. Toner

Ruth Catherine Fong

Tegan Maharaj

Pang Wei Koh

Sara Hooker

Jade Leung

Andrew John Trask

Emma Bluemke

Jonathan Lebensbold

Cullen C. O'keefe

Mark Koren

Th'eo Ryffel … (voir 39 de plus)

JB Rubinovitz

Tamay Besiroglu

Federica Carugati

Jack Clark

Peter Eckersley

Sarah de Haas

Maritza L. Johnson

Ben Laurie

Alex Ingerman

Igor Krawczuk

Amanda Askell

Rosario Cammarota

A. Lohn

David Scott Krueger

Charlotte Stix

Peter Mark Henderson

Logan Graham

Carina E. A. Prunkl

Bianca Martin

Elizabeth Seger

Noa Zilberman

Sean O hEigeartaigh

Frens Kroeger

Girish Sastry

R. Kagan

Adrian Weller

Brian Shek-kam Tse

Elizabeth Barnes

Allan Dafoe

Paul D. Scharre

Ariel Herbert-Voss

Martijn Rasser

Shagun Sodhani

Carrick Flynn

Thomas Krendl Gilbert

Lisa Dyer

Saif M. Khan

Markus Anderljung

2020-04-15

ArXiv (prépublication)

Establishing an evaluation metric to quantify climate change image realism

Sharon Zhou

Alexandra Luccioni

Gautier Cosne

Michael S. Bernstein

2020-04-07

Machine Learning: Science and Technology (publié)

Combating False Negatives in Adversarial Imitation Learning (Student Abstract)

Konrad Żołna

Chitwan Saharia

Léonard Boussioux

David Y. T. Hui

Maxime Chevalier-Boisvert

Dzmitry Bahdanau

2020-04-03

AAAI Conference on Artificial Intelligence (publié)

Pruning for efficient hardware implementations of deep neural networks

Ghouthi Boukli Hacene

Vincent Gripon

Matthieu Arzel

Nicolas Farrugia

Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay

Qicheng Lao

Xiang Jiang

Mohammad Havaei

Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (voir plus)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.

2020-03-09

ArXiv (prépublication)

On the Morality of Artificial Intelligence

Alexandra Luccioni

Examines ethical principles and guidelines that surround machine learning and artificial intelligence.

2020-03-01

IEEE Technology and Society Magazine (publié)

On Catastrophic Interference in Atari 2600 Games

William Fedus

Dibya Ghosh

John D. Martin

Marc Gendron-Bellemare

Hugo Larochelle

Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interf… (voir plus)erence within an environment inhibits learning. We test this hypothesis through a large-scale empirical study in the Arcade Learning Environment (ALE) and, indeed, find supporting evidence. We show that interference causes performance to plateau; the network cannot train on segments beyond the plateau without degrading the policy used to reach there. By synthetically controlling for interference, we demonstrate performance boosts across architectures, learning algorithms and environments. A more refined analysis shows that learning one segment of a game often increases prediction errors elsewhere. Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.

2020-02-28

ArXiv (prépublication)

Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning

Devansh Arpit

Huan Wang

Caiming Xiong

Richard Socher

2020-02-20

ArXiv (prépublication)

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

Michel Deudon

Alfredo Kalaitzis

Israel Goytom

Md Rifat Arefin

Zhichao Lin

Kris Sankaran

Vincent Michalski

Samira Ebrahimi Kahou

Julien Cornebise

Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic res… (voir plus)ults, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.

2020-02-15

ArXiv (prépublication)