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

Berkes Anaïs

Collaborateur·rice de recherche - Cambridge University

Superviseur⋅e principal⋅e :

Rim Assouel

Doctorat - UdeM

Stefan Bauer

Visiteur de recherche indépendant

Co-superviseur⋅e :

Guillaume Lajoie

Shahana Chatterjee

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Loubna Benabbou

Desmond Elliott

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Doctorat

Doctorat - UdeM

Moksh Jain

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni - UdeM

Hyeonah Kim

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Alex Hernández-García

Tabitha Edith Lee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice alumni

Collaborateur·rice alumni - UdeM

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sarthak Mittal

Doctorat - 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

Lena Podina

Collaborateur·rice de recherche - University of Waterloo

Superviseur⋅e principal⋅e :

David Rolnick

Camille Rochefort-Boulanger

Nassim Rahaman

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

Amine RAZIG

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Postdoctorat - UdeM

Postdoctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

Dragos Secrieru

Collaborateur·rice alumni - UdeM

Divya Sharma

Postdoctorat

Co-superviseur⋅e :

Alex Hernández-García

Mélisande Astrid Crystal Teng

Vincent Taboga

Collaborateur·rice alumni - Polytechnique

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

Ivan Titov

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Siva Reddy

Alex Tong

Collaborateur·rice alumni - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

Collaborateur·rice de recherche - UdeM

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

Dinghuai Zhang

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Harry Zhao

Collaborateur·rice alumni - 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

Pre-Training and Fine-Tuning Generative Flow Networks

Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic policies to sequentially generate compositional objects fr… (voir plus)om a given unnormalized reward distribution. They can generate diverse sets of high-reward objects, which is an important consideration in scientific discovery tasks. However, as they are typically trained from a given extrinsic reward function, it remains an important open challenge about how to leverage the power of pre-training and train GFlowNets in an unsupervised fashion for efficient adaptation to downstream tasks. Inspired by recent successes of unsupervised pre-training in various domains, we introduce a novel approach for reward-free pre-training of GFlowNets. By framing the training as a self-supervised problem, we propose an outcome-conditioned GFlowNet (OC-GFN) that learns to explore the candidate space. Specifically, OC-GFN learns to reach any targeted outcomes, akin to goal-conditioned policies in reinforcement learning. We show that the pre-trained OC-GFN model can allow for a direct extraction of a policy capable of sampling from any new reward functions in downstream tasks. Nonetheless, adapting OC-GFN on a downstream task-specific reward involves an intractable marginalization over possible outcomes. We propose a novel way to approximate this marginalization by learning an amortized predictor enabling efficient fine-tuning. Extensive experimental results validate the efficacy of our approach, demonstrating the effectiveness of pre-training the OC-GFN, and its ability to swiftly adapt to downstream tasks and discover modes more efficiently. This work may serve as a foundation for further exploration of pre-training strategies in the context of GFlowNets.

2024-01-15

ICLR.cc/2024/Conference (spotlight)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Usman Anwar

Abulhair Saparov

Javier Rando

Daniel Paleka

Miles Turpin

Peter Hase

Ekdeep Singh Lubana

Erik Jenner

Stephen Casper

Oliver Sourbut

Benjamin L. Edelman

Zhaowei Zhang

Mario Günther

Anton Korinek

Jose Hernandez-Orallo

Lewis Hammond

Eric Bigelow

Alexander Pan

Lauro Langosco

Tomasz Korbak … (voir 22 de plus)

Heidi Zhang

Ruiqi Zhong

Seán Ó hÉigeartaigh

Gabriel Recchia

Giulio Corsi

Alan Chan

Markus Anderljung

Lilian Edwards

Aleksandar Petrov

Christian Schroeder de Witt

Sumeet Ramesh Motwani

Samuel Albanie

Danqi Chen

Philip H.S. Torr

Tegan Maharaj

Jakob Foerster

Florian Tramèr

He He

Atoosa Kasirzadeh

Yejin Choi

David Krueger

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (voir plus)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose

2023-12-31

Trans. Mach. Learn. Res. (publié)

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

Maksym Korablyov

Cheng-Hao Liu

Moksh Jain

Almer Van Der Sloot

Éric Jolicoeur

Edward Ruediger

Andrei Nica

Emmanuel Bengio

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor Ion Butoi

Saikrishna Gottipati

Prateek Gupta

Ladislav Rampasek … (voir 14 de plus)

Sasikanth Avancha

Pierre-Luc Bacon

William Hamilton

Brooks Paige

Sanchit Misra

Stanislaw Jastrzebski

Bharat Kaul

Doina Precup

José Miguel Hernández-Lobato

Marwin Segler

Michael 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… (voir plus)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.

2023-12-31

arXiv (prépublication)

Local Search GFlowNets

Sungsoo Ahn

Jinkyoo Park

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.

2023-12-31

ICLR (publié)

Machine Learning and Information Theory Concepts Towards an AI Mathematician

Nikolay Malkin

The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms … (voir plus)of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.

2023-12-31

arXiv (prépublication)

PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design

Alexandre Duval

Victor Schmidt

Santiago Miret

Alex Hernández-García

David Rolnick

Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electro… (voir plus)chemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the energy spent on such activities, we must quickly discover more efficient catalysts to drive electrochemical reactions. Machine learning (ML) holds the potential to efficiently model materials properties from large amounts of data, accelerating electrocatalyst design. The Open Catalyst Project OC20 dataset was constructed to that end. However, ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. In this paper, we propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy. This includes improvements in (1) the graph creation step, (2) atom representations, (3) the energy prediction head, and (4) the force prediction head. We describe these contributions, referred to as PhAST, and evaluate them thoroughly on multiple architectures. Overall, PhAST improves energy MAE by 4 to 42

2023-12-31

Journal of Machine Learning Research (publié)

Simulation-Free Schrödinger Bridges via Score and Flow Matching

Yanlei Zhang

We present simulation-free score and flow matching ([SF]…

2023-12-31

AISTATS (publié)

proceedings.mlr.press

Sources of Richness and Ineffability for Phenomenally Conscious States

Xu Ji

Eric Elmoznino

George Deane

Axel Constant

Guillaume Dumas

Guillaume Lajoie

Jonathan Simon

Conscious states (states that there is something it is like to be in) seem both rich or full of detail, and ineffable or hard to fully descr… (voir plus)ibe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience: two important aspects that seem to be part of what makes qualitative character so puzzling.

2023-12-31

Neuroscience of Consciousness (publié)

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

Alexandre AGM Duval

Simon V. Mathis

Chaitanya K. Joshi

Victor Schmidt

Santiago Miret

Fragkiskos D. Malliaros

Taco Cohen

Pietro Lio’

Michael M. Bronstein

2023-12-11

ArXiv (prépublication)

Low Compute Unlearning via Sparse Representations

Vedant Shah

Frederik Träuble

Ashish Malik

Hugo Larochelle

Michael Curtis Mozer

Sanjeev Arora

Anirudh Goyal

Machine unlearning, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible … (voir plus)using existing techniques. We propose a low-compute unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the dataset. We evaluate the proposed technique on the problem of class unlearning using four datasets: CIFAR-10, CIFAR-100, LACUNA-100 and ImageNet-1k. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all four datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.

2023-11-25

arXiv (prépublication)

Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles

Luca Scimeca

Alexander Rubinstein

Damien Teney

Seong Joon Oh

Armand Mihai Nicolicioiu

Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (voir plus)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose

2023-11-22

ArXiv (prépublication)

Responses to pattern-violating visual stimuli evolve differently over days in somata and distal apical dendrites

Colleen J. Gillon

Jason E. Pina

Jérôme A. Lecoq

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Yoshua Bengio … (voir 3 de plus)

Timothy P. Lillicrap

Blake A. Richards

Joel Zylberberg

Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In… (voir plus) line with this conjecture, different responses to pattern-matching vs pattern-violating visual stimuli have been observed in both spiking and somatic calcium imaging data. However, it remains unknown whether these pattern-violation signals are different between the distal apical dendrites, which are heavily targeted by top-down signals, and the somata, where bottom-up information is primarily integrated. Furthermore, it is unknown how responses to pattern-violating stimuli evolve over time as an animal gains more experience with them. Here, we address these unanswered questions by analyzing responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons tracked over multiple days in primary visual cortex of awake, behaving female and male mice. We use sequences of Gabor patches with patterns in their orientations to create pattern-matching and pattern-violating stimuli, and two-photon calcium imaging to record neuronal responses. Many neurons in both layers show large differences between their responses to pattern-matching and pattern-violating stimuli. Interestingly, these responses evolve in opposite directions in the somata and distal apical dendrites, with somata becoming less sensitive to pattern-violating stimuli and distal apical dendrites more sensitive. These differences between the somata and distal apical dendrites may be important for hierarchical computation of sensory predictions and learning, since these two compartments tend to receive bottom-up and top-down information, respectively.

2023-11-20

Journal of Neuroscience (publié)