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

Singh Aasheesh

Professional Master's

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

Aayush Bajaj

Professional Master's - Université de Montréal

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Loubna Benabbou

Independent visiting researcher - UQAR

Paul Bertin

PhD - Université de Montréal

Ghait Boukachab

Research Intern - UQAR

Oussama Boussif

PhD - Université de Montréal

Independent visiting researcher - MIT

Xiaoyin Chen

PhD - Université de Montréal

Chen Chen

Postdoctorate - Université de Montréal

Co-supervisor :

Blake Richards

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

PhD - Barcelona University

Piotr Gainski

Research Intern - Université de Montréal

Ahmad Ghawanmeh

Professional Master's - Université de Montréal

Ivan Grega Grega

Collaborating researcher - Université de Montréal

Pietro Greiner

Collaborating researcher

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

Research Intern - Université de Montréal

Collaborating Alumni

Zhen Liu

PhD - Université de Montréal

Principal supervisor :

Liam Paull

Stephen Lu

Research Intern - McGill University

stephen.lu@mila.quebec

Research Intern - Imperial College London

PhD - Université de Montréal

Mohammed Mahfoud

Research Intern - Université de Montréal

Nikolay Malkin

Collaborating Alumni - Université de Montréal

DESS - 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 :

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 :

Sarath Chandar Anbil Parthipan

Nassim Rahaman

PhD - Max-Planck-Institute for Intelligent Systems

Jarrid Rector-Brooks

PhD - Université de Montréal

Co-supervisor :

Danyal REHMAN

Postdoctorate - Université de Montréal

James Requeima

Independent visiting researcher - Université de Montréal

Jessie Richter-Powell

Independent visiting researcher - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

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 :

Todosijevic 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

Elmimouni Zakaria

Research Intern - Université de Montréal

Dinghuai Zhang

PhD - Université de Montréal

Principal supervisor :

Aaron Courville

Ruixiang Zhang

PhD - Université de Montréal

Principal supervisor :

Liam Paull

William Zhang

PhD - Université de Montréal

PhD - McGill University

Principal supervisor :

Mathieu Blanchette

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

Amortizing intractable inference in diffusion models for vision, language, and control

Siddarth Venkatraman

Moksh J. Jain

Luca Scimeca

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Emmanuel Bengio

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

Nikolay Malkin

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

Learning diverse attacks on large language models for robust red-teaming and safety tuning

Seanie Lee

Minsu Kim

Lynn Cherif

David Dobre

Juho Lee

Sung Ju Hwang

Kenji Kawaguchi

Gauthier Gidel

Nikolay Malkin

Moksh J. Jain

Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.

2024-05-28

ArXiv (preprint)

Estimating Expectations without Sampling: Neural Stein Estimation

Mohsin Hasan

Dinghuai Zhang

Cheikh Ahmed

Awa Khouna

We propose a method for estimating the expected value of a given function …

2024-05-27

approximateinference.org/AABI/2024/Symposium (accepted)

Attention as an RNN

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization

Miloš Nikolić

Ghouthi Boukli Hacene

Ciaran Bannon

Alberto Delmas Lascorz

Matthieu Courbariaux

Omar Mohamed Awad

Isak Edo Vivancos

Vincent Gripon

Andreas Moshovos

Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time … (see more)and energy benefits on existing hardware designs that support short bitlengths. However, the question of finding the minimum bitlength for a desired accuracy remains open. We introduce a training method for minimizing inference bitlength at any granularity while maintaining accuracy. Namely, we propose a regularizer that penalizes large bitlength representations throughout the architecture and show how it can be modified to minimize other quantifiable criteria, such as number of operations or memory footprint. We demonstrate that our method learns thrifty representations while maintaining accuracy. With ImageNet, the method produces an average per layer bitlength of 4.13, 3.76 and 4.36 bits on AlexNet, ResNet18 and MobileNet V2 respectively, remaining within 2.0%, 0.5% and 0.5% of the base TOP-1 accuracy.

2024-05-19

2024 IEEE International Symposium on Circuits and Systems (ISCAS) (published)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare-Pepin

Franccois Lespinasse

Philipp Thölke

Yann Harel

Kory Wallace Mathewson

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (see 5 more)

Mississauga

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (see more)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (preprint)

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems

David Dalrymple

David

Joar Max Viktor Skalse

Stuart Russell

Max Tegmark

Sanjit A. Seshia

Steve Omohundro

Christian Szegedy

Ben Goldhaber

Nora Ammann

Alessandro Abate

Joe Halpern

Clark Barrett

Ding Zhao

Zhi-Xuan Tan

Jeannette Wing

Joshua B. Tenenbaum

Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with … (see more)a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.

2024-05-10

ArXiv (preprint)

Generative Active Learning for the Search of Small-molecule Protein Binders

Maksym Korablyov

Cheng-Hao Liu

Moksh J. Jain

Almer M. van der Sloot

Eric Jolicoeur

Edward Ruediger

Andrei Cristian Nica

Emmanuel Bengio

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor I Butoi

Jarrid Rector-Brooks

Simon R. Blackburn

Leo Feng

Hadi Nekoei

Sai Krishna Gottipati

Priyesh Vijayan

Prateek Gupta

Ladislav Rampášek … (see 14 more)

Sasikanth Avancha

Pierre-Luc Bacon

William L. Hamilton

Brooks Paige

Sanchit Misra

Stanisław Jastrzębski

Bharat Kaul

Doina Precup

Jos'e Miguel Hern'andez-Lobato

Marwin Segler

Michael M. Bronstein

Anne Marinier

Mike Tyers

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (see more)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

2024-05-02

ArXiv (preprint)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Tara Akhound-Sadegh

Jarrid Rector-Brooks

Joey Bose

Sarthak Mittal

Pablo Lemos

Cheng-Hao Liu

Marcin Sendera

Siamak Ravanbakhsh

Gauthier Gidel

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (see more)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient---and no data samples---to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is *simulation-free*, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-05-01

ICML.cc/2024/Conference (poster)

Learning to Scale Logits for Temperature-Conditional GFlowNets

Minsu Kim

Joohwan Ko

Dinghuai Zhang

Ling Pan

Taeyoung Yun

Woo Chang Kim

Jinkyoo Park

Emmanuel Bengio

GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, tempera… (see more)ture-conditional GFlowNets can introduce temperature-based controllability for exploration and exploitation. We propose \textit{Logit-scaling GFlowNets} (Logit-GFN), a novel architectural design that greatly accelerates the training of temperature-conditional GFlowNets. It is based on the idea that previously proposed approaches introduced numerical challenges in the deep network training, since different temperatures may give rise to very different gradient profiles as well as magnitudes of the policy's logits. We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. Also, using Logit-GFN, GFlowNets can be improved by having better generalization capabilities in offline learning and mode discovery capabilities in online learning, which is empirically verified in various biological and chemical tasks. Our code is available at https://github.com/dbsxodud-11/logit-gfn

2024-05-01

ICML.cc/2024/Conference (poster)

Memory Efficient Neural Processes via Constant Memory Attention Block

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty. Recent state-of-the-art methods, … (see more)however, leverage expensive attention mechanisms, limiting their applications, particularly in low-resource settings. In this work, we propose Constant Memory Attention Block (CMAB), a novel general-purpose attention block that (1) is permutation invariant, (2) computes its output in constant memory, and (3) performs updates in constant computation. Building on CMAB, we propose Constant Memory Attentive Neural Processes (CMANPs), an NP variant which only requires \textbf{constant} memory. Empirically, we show CMANPs achieve state-of-the-art results on popular NP benchmarks (meta-regression and image completion) while being significantly more memory efficient than prior methods.

2024-05-01

ICML.cc/2024/Conference (poster)

Discrete Probabilistic Inference as Control in Multi-path Environments

Tristan Deleu

Padideh Nouri

Nikolay Malkin

Doina Precup

We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to … (see more)find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward. While we could use maximum entropy Reinforcement Learning (MaxEnt RL) to solve this problem for some distributions, it has been shown that in general, the distribution over states induced by the optimal policy may be biased in cases where there are multiple ways to generate the same object. To address this issue, Generative Flow Networks (GFlowNets) learn a stochastic policy that samples objects proportionally to their reward by approximately enforcing a conservation of flows across the whole Markov Decision Process (MDP). In this paper, we extend recent methods correcting the reward in order to guarantee that the marginal distribution induced by the optimal MaxEnt RL policy is proportional to the original reward, regardless of the structure of the underlying MDP. We also prove that some flow-matching objectives found in the GFlowNet literature are in fact equivalent to well-established MaxEnt RL algorithms with a corrected reward. Finally, we study empirically the performance of multiple MaxEnt RL and GFlowNet algorithms on multiple problems involving sampling from discrete distributions.

2024-04-26

auai.org/UAI/2024/Conference (poster)