Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Directeur scientifique, Équipe de direction
Observateur, Conseil d'administration, Mila

Biographie

*Pour toute demande média, veuillez écrire à medias@mila.quebec.

Pour plus d’information, contactez Julie Mongeau, adjointe de direction à julie.mongeau@mila.quebec.

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 directeur 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 directeur scientifique 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

Maîtrise professionnelle - Université de Montréal
Co-superviseur⋅e :
Maîtrise professionnelle - Université de Montréal
Doctorat - Université de Montréal
Postdoctorat - Université de Montréal
Co-superviseur⋅e :
Postdoctorat - Université de Montréal
Doctorat - Université de Montréal
Collaborateur·rice de recherche - Université Paris-Saclay
Superviseur⋅e principal⋅e :
Maîtrise professionnelle - Université de Montréal
Visiteur de recherche indépendant - MIT
Doctorat - École Polytechnique Montréal Fédérale de Lausanne
Stagiaire de recherche - Université du Québec à Rimouski
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Postdoctorat - Université de Montréal
Co-superviseur⋅e :
Maîtrise professionnelle - Université de Montréal
Doctorat - Université de Montréal
Co-superviseur⋅e :
Doctorat - Barcelona University
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Postdoctorat - Université de Montréal
Co-superviseur⋅e :
Maîtrise recherche - Université de Montréal
Doctorat - Université de Montréal
Stagiaire de recherche - Université de Montréal
Doctorat - Université de Montréal
Co-superviseur⋅e :
Stagiaire de recherche - UQAR
Collaborateur·rice alumni
Visiteur de recherche indépendant - Université de Montréal
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Stagiaire de recherche - McGill University
Visiteur de recherche indépendant - Université de Montréal
Doctorat - Université de Montréal
Co-superviseur⋅e :
Doctorat - Université de Montréal
Co-superviseur⋅e :
Maîtrise professionnelle - Université de Montréal
Stagiaire de recherche - Université de Montréal
Doctorat - Université de Montréal
Doctorat - Massachusetts Institute of Technology
Doctorat - Université de Montréal
Doctorat - Université de Montréal
Visiteur de recherche indépendant - Technical University Munich (TUM)
Visiteur de recherche indépendant - Hong Kong University of Science and Technology (HKUST)
DESS - Université de Montréal
Visiteur de recherche indépendant - UQAR
Postdoctorat - Université de Montréal
Doctorat - Université de Montréal
Stagiaire de recherche - Université de Montréal
Visiteur de recherche indépendant - Technical University of Munich
Stagiaire de recherche - Imperial College London
Doctorat - Université de Montréal
Co-superviseur⋅e :
Postdoctorat - Université de Montréal
Doctorat - McGill University
Superviseur⋅e principal⋅e :
Maîtrise professionnelle - Université de Montréal
Collaborateur·rice de recherche - Université de Montréal
Stagiaire de recherche - Université de Montréal
Stagiaire de recherche - Université de Montréal
Doctorat - Université de Montréal
Doctorat - Max-Planck-Institute for Intelligent Systems
Doctorat - McGill University
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - Université de Montréal
Maîtrise professionnelle - Université de Montréal
Doctorat - Université de Montréal
Visiteur de recherche indépendant - Université de Montréal
Collaborateur·rice alumni - Université de Montréal
Collaborateur·rice de recherche
Maîtrise professionnelle - Université de Montréal
Collaborateur·rice de recherche - Valence
Superviseur⋅e principal⋅e :
Doctorat - Université de Montréal
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Doctorat - Université de Montréal
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Stagiaire de recherche - Université de Montréal
Collaborateur·rice de recherche - Université de Montréal
Visiteur de recherche indépendant
Co-superviseur⋅e :
Postdoctorat - Université de Montréal
Stagiaire de recherche - McGill University
Maîtrise professionnelle - Université de Montréal
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
Maîtrise recherche - Université de Montréal
Co-superviseur⋅e :
Doctorat - Université de Montréal
Maîtrise recherche - Université de Montréal
Doctorat - Université de Montréal
Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Superviseur⋅e principal⋅e :
Baccalauréat - Université de Montréal
Doctorat - Université de Montréal
Maîtrise professionnelle - Université de Montréal
Maîtrise professionnelle - Université de Montréal
Stagiaire de recherche - Université de Montréal
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Maîtrise professionnelle - Université de Montréal
Postdoctorat - Université de Montréal

Publications

Meta-learning framework with applications to zero-shot time-series forecasting
Boris Oreshkin
Dmitri Carpov
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.
COVI White Paper-Version 1.1
Hannah Alsdurf
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler J. Kolody
Sekoul Krastev
Robert Obryk
Dan Pilat
Valerie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Jean-franois Rousseau
abhinav sharma
Brooke Struck … (voir 3 de plus)
Martin Weiss
Yun William Yu
The SARS-CoV-2 (Covid-19) pandemic has resulted in significant strain on health care and public health institutions around the world. Contac… (voir plus)t tracing is an essential tool for public health officials and local communities to change the course of the Covid-19 pandemic. Standard manual contact tracing of people infected with Covid-19, while the current gold standard, has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile applications has the potential to shift the paradigm of Covid-19 community spread. Although some countries have deployed centralized tracking systems through either GPS or Bluetooth, more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or in for-profit corporations. Additionally, machine learning methods can be used to circumvent some of the limitations of standard digital tracing by incorporating many clues (including medical conditions, self-reported symptoms, and numerous encounters with people at different risk levels, for different durations and distances) and their uncertainty into a more graded and precise estimation of infection and contagion risk. The estimated risk can be used to provide early risk awareness, personalized recommendations and relevant information to the user and connect them to health services. Finally, the non-identifying data about these risks can inform detailed epidemiological models trained jointly with the machine learning predictor, and these models can provide statistical evidence for the interaction and importance of different factors involved in the transmission of the disease. They can also be used to monitor, evaluate and optimize different health policy and confinement/deconfinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of ‘COVI,’ a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada. Addendum 2020-07-14: The government of Canada has declined to endorse COVI and will be promoting a different app for decentralized contact tracing. In the interest of preventing fragmentation of the app landscape, COVI will therefore not be deployed to end users. We are currently still in the process of finalizing the project, and plan to release our code and models for academic consumption and to make them accessible to other States should they wish to deploy an app based on or inspired by said code and models. University of Ottawa, Mila, Université de Montréal, The Alan Turing Institute, University of Oxford, University of Pennsylvania, McGill University, Borden Ladner Gervais LLP, The Decision Lab, HEC Montréal, Max Planck Institute, Libéo, University of Toronto. Corresponding author general: richard.janda@mcgill.ca Corresponding author for public health: abhinav.sharma@mcgill.ca Corresponding author for privacy: ywyu@math.toronto.edu Corresponding author for machine learning: yoshua.bengio@mila.quebec Corresponding author for user perspective: brooke@thedecisionlab.com Corresponding author for technical implementation: jean-francois.rousseau@libeo.com 1 ar X iv :2 00 5. 08 50 2v 2 [ cs .C R ] 2 7 Ju l 2 02 0
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Boris Oreshkin
Dmitri Carpov
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based o… (voir plus)n backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
On the interplay between noise and curvature and its effect on optimization and generalization
Valentin Thomas
Fabian Pedregosa
Bart van Merriënboer
Pierre-Antoine Manzagol
The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the v… (voir plus)ariance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed. Further, as the ultimate goal is good generalization performance, we clarify how both curvature and noise are relevant to properly estimate the generalization gap. Realizing that the limitations of some existing works stems from a confusion between these matrices, we also clarify the distinction between the Fisher matrix, the Hessian, and the covariance matrix of the gradients.
A deep learning framework for neuroscience
Timothy P. Lillicrap
Philippe Beaudoin
Rafal Bogacz
Amelia Christensen
Claudia Clopath
Rui Ponte Costa
Archy de Berker
Surya Ganguli
Colleen J Gillon
Danijar Hafner
Adam Kepecs
Nikolaus Kriegeskorte
Peter Latham
Grace W. Lindsay
Kenneth D. Miller
Richard Naud
Christopher C. Pack
Panayiota Poirazi … (voir 12 de plus)
Pieter Roelfsema
João Sacramento
Andrew Saxe
Benjamin Scellier
Anna C. Schapiro
Walter Senn
Greg Wayne
Daniel Yamins
Friedemann Zenke
Joel Zylberberg
Denis Therien
Konrad Paul Kording
Interpolation Consistency Training for Semi-Supervised Learning
Vikas Verma
Kenji Kawaguchi
Alex Lamb
Juho Kannala
David Lopez-Paz
Arno Solin
On the interplay between noise and curvature and its effect on optimization and generalization
Valentin Thomas
Fabian Pedregosa
Bart van Merriënboer
Pierre-Antoine Mangazol
The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the v… (voir plus)ariance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed. Further, as the ultimate goal is good generalization performance, we clarify how both curvature and noise are relevant to properly estimate the generalization gap. Realizing that the limitations of some existing works stems from a confusion between these matrices, we also clarify the distinction between the Fisher matrix, the Hessian, and the covariance matrix of the gradients.
Information matrices and generalization
Valentin Thomas
Fabian Pedregosa
Bart van Merriënboer
Pierre-Antoine Manzagol
This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically… (voir plus) demonstrate the effectiveness of the Takeuchi information criterion (TIC), an extension of the Akaike information criterion (AIC) for misspecified models, in estimating the generalization gap, shedding light on why quantities such as the number of parameters cannot quantify generalization. The TIC depends on both the Hessian of the loss H and the covariance of the gradients C. By exploring the similarities and differences between these two matrices as well as the Fisher information matrix F, we study the interplay between noise and curvature in deep models. We also address the question of whether C is a reasonable approximation to F, as is commonly assumed.
Information matrices and generalization
Valentin Thomas
Fabian Pedregosa
Bart van Merriënboer
Pierre-Antoine Manzagol
This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically… (voir plus) demonstrate the effectiveness of the Takeuchi information criterion (TIC), an extension of the Akaike information criterion (AIC) for misspecified models, in estimating the generalization gap, shedding light on why quantities such as the number of parameters cannot quantify generalization. The TIC depends on both the Hessian of the loss H and the covariance of the gradients C. By exploring the similarities and differences between these two matrices as well as the Fisher information matrix F, we study the interplay between noise and curvature in deep models. We also address the question of whether C is a reasonable approximation to F, as is commonly assumed.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Boris Oreshkin
Dmitri Carpov
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based o… (voir plus)n backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
Interpolation Consistency Training for Semi-Supervised Learning
Vikas Verma
Alex Lamb
Juho Kannala
David Lopez-Paz
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy
Alex Lamb
Vikas Verma
Juho Kannala
Adversarial robustness has become a central goal in deep learning, both in theory and practice. However, successful methods to improve adver… (voir plus)sarial robustness (such as adversarial training) greatly hurt generalization performance on the clean data. This could have a major impact on how adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve performance on the clean data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases clean test error from 5.8% to 16.7%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a clean test error of only 6.5%. With our technique, the relative error increase for the robust model is reduced from 187.9% to just 12.1%.