Yoshua Bengio

Biography

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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 advisor 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 special advisor and founding 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

Collaborating Alumni - McGill University

Mohammed Abukalam

Collaborating Alumni - Université de Montréal

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

Rim Assouel

PhD - Université de Montréal

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Paul Bertin

PhD - Université de Montréal

Joyce Chai

Independent visiting researcher

Principal supervisor :

Siva Reddy

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

David Rolnick

Xiaoyin Chen

PhD - Université de Montréal

Sanghyeok Choi

Collaborating researcher - KAIST

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

Collaborating Alumni - Université de Montréal

Co-supervisor :

Loubna Benabbou

Desmond Elliott

Independent visiting researcher

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

Leo Feng

PhD - Université de Montréal

PhD

PhD - Université de Montréal

Edward Hu

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Collaborating Alumni - Université de Montréal

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernandez-Garcia

Salem Lahlou

Collaborating Alumni - Université de Montréal

Tabitha Edith Lee

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating Alumni

Zhen Liu

Collaborating Alumni - Université de Montréal

Principal supervisor :

Liam Paull

Kanika Madan

PhD - Université de Montréal

Nikolay Malkin

Collaborating Alumni - Université de Montréal

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

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

Padideh Nouri

PhD - Université de Montréal

Principal supervisor :

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

Lena Podina

Collaborating researcher - University of Waterloo

Principal supervisor :

David Rolnick

Nassim Rahaman

Collaborating Alumni - Max-Planck-Institute for Intelligent Systems

Amine RAZIG

Collaborating researcher - Université de Montréal

Co-supervisor :

Loubna Benabbou

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

James Requeima

Independent visiting researcher - Université de Montréal

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

Abhik Roychoudhury Roychoudhury

Independent visiting researcher

Principal supervisor :

Siva Reddy

Luca Scimeca

Postdoctorate - Université de Montréal

Collaborating Alumni - Université de Montréal

Marcin Sendera

Collaborating Alumni - Université de Montréal

Divya Sharma

Postdoctorate

Co-supervisor :

Alex Hernandez-Garcia

Mélisande Astrid Crystal Teng

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

Ivan Titov

Independent visiting researcher

Principal supervisor :

Siva Reddy

Alex Tong

Collaborating Alumni - Université de Montréal

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher

Collaborating researcher - Université de Montréal

Nicole Zhang

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Aaron Courville

Tianyu Zhang

PhD - Université de Montréal

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Harry Zhao

Collaborating Alumni - 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

Exploring the Wasserstein metric for survival analysis

Tristan Sylvain

Margaux Luck

Joseph Paul Cohen

Andrea Lodi

Survival analysis is a type of semi-supervised task where the target output (the survival time) is often right-censored. Utilizing this info… (see more)rmation is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions can take advantage of this information: partial likelihood methods, rank methods, and our own classiﬁcation method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier (KM) estimate of the probability density to impute the labels of censored examples. The proposed method predicts the probability distribution of an event, letting us compute survival curves and expected times of survival that are easier to interpret than the rank. We also demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for survival models.

Exploring the Wasserstein metric for time-to-event analysis.

Tristan Sylvain

Margaux Luck

Joseph Paul Cohen

Heloise Cardinal

Andrea Lodi

2021-01-01

SPACA (published)

proceedings.mlr.press

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments

Anirudh Goyal

Alex Lamb

Phanideep Gampa

Philippe Beaudoin

Charles Blundell

Sergey Levine

Michael Curtis Mozer

2021-01-01

ICLR (published)

Fast and Slow Learning of Recurrent Independent Mechanisms

Kanika Madan

Nan Rosemary Ke

Anirudh Goyal

Bernhard Schölkopf

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (see more)nt interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.

2021-01-01

ICLR (published)

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Moksh J. Jain

This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions… (see more), such that the probability of generating an object is proportional to a given positive reward for that object. Whereas standard return maximization tends to converge to a single return-maximizing sequence, there are cases where we would like to sample a diverse set of high-return solutions. These arise, for example, in black-box function optimization when few rounds are possible, each with large batches of queries, where the batches should be diverse, e.g., in the design of new molecules. One can also see this as a problem of approximately converting an energy function to a generative distribution. While MCMC methods can achieve that, they are expensive and generally only perform local exploration. Instead, training a generative policy amortizes the cost of search during training and yields to fast generation. Using insights from Temporal Difference learning, we propose GFlowNet, based on a view of the generative process as a flow network, making it possible to handle the tricky case where different trajectories can yield the same final state, e.g., there are many ways to sequentially add atoms to generate some molecular graph. We cast the set of trajectories as a flow and convert the flow consistency equations into a learning objective, akin to the casting of the Bellman equations into Temporal Difference methods. We prove that any global minimum of the proposed objectives yields a policy which samples from the desired distribution, and demonstrate the improved performance and diversity of GFlowNet on a simple domain where there are many modes to the reward function, and on a molecule synthesis task.

Jean-christophe Gagnon-audet

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

Kartik Ahuja

Ethan Caballero

Dinghuai Zhang

Ioannis Mitliagkas

Irina Rish

The invariance principle from causality is at the heart of notable approaches such as invariant risk minimization (IRM) that seek to address… (see more) out-of-distribution (OOD) generalization failures. Despite the promising theory, invariance principle-based approaches fail in common classification tasks, where invariant (causal) features capture all the information about the label. Are these failures due to the methods failing to capture the invariance? Or is the invariance principle itself insufficient? To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD. In contrast to the linear regression tasks, we show that for linear classification tasks we need much stronger restrictions on the distribution shifts, or otherwise OOD generalization is impossible. Furthermore, even with appropriate restrictions on distribution shifts in place, we show that the invariance principle alone is insufficient. We prove that a form of the information bottleneck constraint along with invariance helps address key failures when invariant features capture all the information about the label and also retains the existing success when they do not. We propose an approach that incorporates both of these principles and demonstrate its effectiveness in several experiments.

Learning Neural Generative Dynamics for Molecular Conformation Generation

Minkai Xu

Shitong Luo

Jian Peng

Jian Tang

We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamic… (see more)s, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling.

2021-01-01

ICLR (published)

Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon

Andrea Lodi

Antoine Prouvost

2021-01-01

Eur. J. Oper. Res. (published)

doi.org

arxiv.org

Multi-Domain Balanced Sampling Improves Out-of-Generalization of Chest X-ray Pathology Prediction Models

Enoch Amoatey Tetteh

Joseph D Viviano

David Scott Krueger

Joseph Paul Cohen

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (see more)ve been several proposals for efﬁcient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing. Code for this work is available on Github. 1

Multimodal Audio-textual Architecture for Robust Spoken Language Understanding

Dmitriy Serdyuk

Yongqiang Wang

Christian Fue-730

Anuj Kumar

Baiyang Liu

Edwin Simonnet

Sahar Ghannay

Nathalie Camelin

Tandem spoken language understanding 001 (SLU) systems suffer from the so-called 002 automatic speech recognition (ASR) error 003 propagatio… (see more)n problem. Additionally, as the 004 ASR is not optimized to extract semantics, but 005 solely the linguistic content, relevant semantic 006 cues might be left out of its transcripts. In 007 this work, we propose a multimodal language 008 understanding (MLU) architecture to mitigate 009 these problems. Our solution is based on 010 two compact unidirectional long short-term 011 memory (LSTM) models that encode speech 012 and text information. A fusion layer is also 013 used to fuse audio and text embeddings. 014 Two fusion strategies are explored: a simple 015 concatenation of these embeddings and a 016 cross-modal attention mechanism that learns 017 the contribution of each modality. The ﬁrst 018 approach showed to be the optimal solution 019 to robustly extract semantic information from 020 audio-textual data. We found that attention 021 is less effective at testing time when the text 022 modality is corrupted. Our model is evaluated 023 on three SLU datasets and robustness is tested 024 using ASR outputs from three off-the-shelf 025 ASR engines. Results show that the proposed 026 approach effectively mitigates the ASR error 027 propagation problem for all datasets. 028

Optimization of Artificial Neural Network Hyperparameters For Processing Retrospective Information

A. Rogachev

F. Scholle

Yann Lecun

I. L. Kashirin

M. Demchenko

. Justification of the selection of the architecture and hyperparameters of artificial neural networks (ANN), focused on solving various cla… (see more)sses of applied problems, is a scientific and methodological problem. Optimizing the selection of ANN hyperparameters allows you to improve the quality and speed of ANN training. Various methods of optimizing the selection of ANN hyper-parameters are known – the use of evolutionary calculations, genetic algorithms, etc., but they require the use of additional software. To optimize the process of selecting ANN hyperparameters, Google Research has developed the KerasTuner software tool. It is a platform for automated search of a set of optimal combinations of hyperparameters. In Kerastuner, you can use various methods - random search, Bayesian optimization, or Hyperband. In the numerical experiments conducted by the author, 14 hyperparameters were varied, including the number of blocks of convolutional layers and the filters forming them, the type of activation function, the parameters of the "dropout" layers, and others. The studied tools demonstrated high efficiency while simultaneously varying more than a dozen optimized parameters of the convolutional network. The calculation time on the Colaboratory platform for the various combined ANN architectures studied, including recurrent RNN networks, was several hours, even with the use of GPU graphics accelerators. For ANN, focused on the processing and recognition of retrospective information, an increase in the quality of recognition was achieved to 80 ... 95%.

Predicting Unreliable Predictions by Shattering a Neural Network

Xu Ji

Razvan Pascanu

(Rex) Devon Hjelm

Andrea Vedaldi

Balaji Lakshminarayanan

Piecewise linear neural networks can be split into subfunctions, each with its own activation pattern, domain, and empirical error. Empirica… (see more)l error for the full network can be written as an expectation over empirical error of subfunctions. Constructing a generalization bound on subfunction empirical error indicates that the more densely a subfunction is surrounded by training samples in representation space, the more reliable its predictions are. Further, it suggests that models with fewer activation regions generalize better, and models that abstract knowledge to a greater degree generalize better, all else equal. We propose not only a theoretical framework to reason about subfunction error bounds but also a pragmatic way of approximately evaluating it, which we apply to predicting which samples the network will not successfully generalize to. We test our method on detection of misclassiﬁcation and out-of-distribution samples, ﬁnding that it performs competitively in both cases. In short, some network activation patterns are associated with higher reliability than others, and these can be identiﬁed using subfunction error bounds.

2021-01-01

arXiv.org (preprint)