Yoshua Bengio

Biography

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For more information please contact Julie Mongeau, executive assistant at julie.mongeau@mila.quebec.

Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific director of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Jamal Abou Haibeh

Research Intern - McGill University

Mohammed Abukalam

Research Intern - Université de Montréal

Rim Assouel

PhD - Université de Montréal

Collaborating Alumni

Research Intern - Université du Québec à Rimouski

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Paul Bertin

PhD - Université de Montréal

Ghait Boukachab

Research Intern - UQAR

Oussama Boussif

PhD - Université de Montréal

Independent visiting researcher - MIT

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

Xiaoyin Chen

PhD - Université de Montréal

Chen Chen

Postdoctorate - Université de Montréal

Co-supervisor :

Blake Richards

Sanghyeok Choi

Collaborating researcher - KAIST

Pierre-Paul De Breuck

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Collaborating researcher - Université Paris-Saclay

Principal supervisor :

Eric Elmoznino

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Katie Everett

PhD - Massachusetts Institute of Technology

Léna Nehale Ezzine

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

Co-supervisor :

Leo Feng

PhD - Université de Montréal

Research Intern - Barcelona University

Piotr Gainski

Research Intern - Université de Montréal

Ivan Grega

Collaborating researcher - Université de Montréal

Pietro Greiner

Research Intern

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

Alex Hernandez-Garcia

Postdoctorate - Université de Montréal

Co-supervisor :

Leon Hetzel

Independent visiting researcher - Technical University Munich (TUM)

Edward Hu

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

Research Intern - Université de Montréal

Master's Research - Université de Montréal

Co-supervisor :

Research Intern - Université de Montréal

Minsu Kim

Collaborating researcher - Université de Montréal

PhD - Université de Montréal

Michał Koziarski

Postdoctorate - Université de Montréal

Salem Lahlou

PhD - Université de Montréal

Hae-Beom Lee

Collaborating Alumni

Seanie Lee

Collaborating Alumni - Université de Montréal

Zhen Liu

PhD - Université de Montréal

Principal supervisor :

Collaborating Alumni

Research Intern - Imperial College London

PhD - Université de Montréal

Mohammed Mahfoud

Research Intern - Université de Montréal

Nikolay Malkin

Collaborating Alumni - Université de Montréal

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Stefano Massaroli

Postdoctorate - Université de Montréal

Collaborating Alumni

Collaborating researcher - Université de Montréal

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Principal supervisor :

Independent visiting researcher - Université de Montréal

Ling Pan

Independent visiting researcher - Hong Kong University of Science and Technology (HKUST)

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

Lena Podina

PhD - University of Waterloo

Principal supervisor :

Nassim Rahaman

PhD - Max-Planck-Institute for Intelligent Systems

Jarrid Rector-Brooks

PhD - Université de Montréal

Co-supervisor :

Sarath Chandar

Danyal REHMAN

Postdoctorate - Université de Montréal

James Requeima

Independent visiting researcher - Université de Montréal

Oli RICHARDSON

Postdoctorate - Université de Montréal

Jessie Richter-Powell

Independent visiting researcher - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

agassoussisalwane2@gmail.com

Salwane Salwane

Research Intern - Université de Montréal

Theo Saulus

Collaborating researcher

Principal supervisor :

Victor Schmidt

PhD - Université de Montréal

Postdoctorate - Université de Montréal

Master's Research - Université de Montréal

Marcin Sendera

Research Intern - Université de Montréal

Dounia Shaaban Kabakibo

Research Intern - Université de Montréal

Vedant Shah

Master's Research - Université de Montréal

Collaborating Alumni

Marco Stock

Independent visiting researcher - Technical University of Munich

marco.stock@tum.de

Anja Surina

PhD - École Polytechnique Montréal Fédérale de Lausanne

Vincent Taboga

Postdoctorate - Polytechnique Montréal

Co-supervisor :

Pierre-Luc Bacon

Mélisande Astrid Crystal Teng

PhD - Université de Montréal

Co-supervisor :

Collaborating researcher

Principal supervisor :

alexander.tong@mila.quebec

Alex Tong

Postdoctorate - Université de Montréal

Collaborating researcher - Valence

Principal supervisor :

Dominique Beaini

Donna Vakalis

Postdoctorate - Université de Montréal

Co-supervisor :

Viktor Viktor Todosijevic

Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Principal supervisor :

Sasha Volokhova

PhD - Université de Montréal

Zichao Yan

Collaborating Alumni - Université de Montréal

Taeyoung YUN

Collaborating researcher - KAIST

Elmimouni Zakaria

Research Intern - Université de Montréal

Tianyu Zhang

PhD - Université de Montréal

PhD - McGill University

Principal supervisor :

Mathieu Blanchette

Dinghuai Zhang

PhD - Université de Montréal

Principal supervisor :

Aaron Courville

Ruixiang Zhang

PhD - Université de Montréal

Principal supervisor :

Liam Paull

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Harry Zhao

PhD - McGill University

Principal supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

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

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

A deep learning framework for neuroscience

Blake Richards

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 … (see 12 more)

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

2019-10-28

Nature Neuroscience (published)

Interpolation Consistency Training for Semi-Supervised Learning

Vikas Verma

Kenji Kawaguchi

Alex Lamb

Juho Kannala

David Lopez-Paz

Arno Solin

2019-08-10

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (published)

On the interplay between noise and curvature and its effect on optimization and generalization

Valentin Thomas

Fabian Pedregosa

Bart van Merriënboer

Pierre-Antoine Mangazol

Nicolas Le Roux

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… (see more)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.

2019-06-19

ArXiv (preprint)

Information matrices and generalization

Valentin Thomas

Fabian Pedregosa

Bart van Merriënboer

Pierre-Antoine Manzagol

Nicolas Le Roux

This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically… (see more) 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.

2019-06-18

arXiv.org (preprint)

Information matrices and generalization

Valentin Thomas

Fabian Pedregosa

Bart van Merriënboer

Pierre-Antoine Manzagol

Nicolas Le Roux

2019-06-18

arXiv.org (preprint)

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Boris Oreshkin

Dmitri Carpov

Nicolas Chapados

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based o… (see more)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.

2019-05-24

ArXiv (preprint)

Interpolation Consistency Training for Semi-Supervised Learning

Vikas Verma

Alex Lamb

Juho Kannala

David Lopez-Paz

2019-03-09

ArXiv (preprint)

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… (see more)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%.

2019-01-01

arXiv.org (preprint)

Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy

Alex Lamb

Vikas Verma

Juho Kannala

2019-01-01

arXiv.org (preprint)

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

Eric P. Larsen

Sébastien Lachapelle

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a method… (see more)ology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming, where the second stage is demanding computationally. We aim to predict at a high speed the expected TDOS associated with the second-stage problem, conditionally on the first-stage variables. This may be used in support of the solution to the overall two-stage problem by avoiding the online generation of multiple second-stage scenarios and solutions. We formulate the tactical prediction problem as a stochastic optimal prediction program, whose solution we approximate with supervised machine learning. The training data set consists of a large number of deterministic operational problems generated by controlled probabilistic sampling. The labels are computed based on solutions to these problems (solved independently and offline), employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application on load planning for rail transportation show that deep learning models produce accurate predictions in very short computing time (milliseconds or less). The predictive accuracy is close to the lower bounds calculated based on sample average approximation of the stochastic prediction programs.

2018-07-31

ArXiv (preprint)

Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation 1

Mohammad Havaei

Nicolas Guizard

Nicolas Chapados

2018-07-04

Signal Processing and Machine Learning for Biomedical Big Data (published)

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke

Konrad Żołna

Alessandro Sordoni

Zhouhan Lin

Adam Trischler

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most o… (see more)f these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing RNN encoders for sequence modelling tasks which allows them to attend to key parts of the input as needed. We formulate this using a multi-layer conditional sequence encoder that reads in one token at a time and makes a discrete decision on whether the token is relevant to the context or question being asked. The discrete gating mechanism takes in the context embedding and the current hidden state as inputs and controls information flow into the layer above. We train it using policy gradient methods. We evaluate this method on several types of tasks with different attributes. First, we evaluate the method on synthetic tasks which allow us to evaluate the model for its generalization ability and probe the behavior of the gates in more controlled settings. We then evaluate this approach on large scale Question Answering tasks including the challenging MS MARCO and SearchQA tasks. Our models shows consistent improvements for both tasks over prior work and our baselines. It has also shown to generalize significantly better on synthetic tasks as compared to the baselines.

2018-07-03

Proceedings of the 35th International Conference on Machine Learning (published)

proceedings.mlr.press