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 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

Jamal Abou Haibeh

Stagiaire de recherche - McGill

Mohammed Abukalam

Stagiaire de recherche - UdeM

agassoussisalwane2@gmail.com

Salwane Agassoussi

Stagiaire de recherche - UdeM

Rim Assouel

Doctorat - UdeM

Ayoub Atanane

Stagiaire de recherche - Université du Québec à Rimouski

Stefan Bauer

Visiteur de recherche indépendant

Co-superviseur⋅e :

Guillaume Lajoie

Paul Bertin

Doctorat - UdeM

Ghait Boukachab

Collaborateur·rice alumni - UQAR

Doctorat - UdeM

Collaborateur·rice de recherche - N/A

Superviseur⋅e principal⋅e :

David Rolnick

Xiaoyin Chen

Doctorat - UdeM

Sanghyeok Choi

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Doctorat - UdeM

Stagiaire de recherche - UdeM

Eric Elmoznino

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - Massachusetts Institute of Technology

Léna Ezzine

Doctorat - UdeM

Jean-Pierre Falet

Doctorat - UdeM

Co-superviseur⋅e :

Leo Feng

Doctorat - UdeM

Stagiaire de recherche - Barcelona University

Piotr Gainski

Stagiaire de recherche - UdeM

Ivan Grega

Stagiaire de recherche - UdeM

Pietro Greiner

Stagiaire de recherche

Mohsin Hasan

Doctorat - UdeM

mohsin.hasan@mila.quebec

Alex Hernandez-Garcia

Postdoctorat - UdeM

Co-superviseur⋅e :

Edward Hu

Doctorat - UdeM

Moksh Jain

Doctorat - UdeM

moksh.jain@mila.quebec

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Stagiaire de recherche - UdeM

Minsu Kim

Collaborateur·rice de recherche - UdeM

Collaborateur·rice alumni - UdeM

Seanie Lee

Collaborateur·rice alumni - UdeM

Collaborateur·rice alumni

Zhen Liu

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Matt MacDermott

Stagiaire de recherche - Imperial College London

Doctorat - UdeM

Stagiaire de recherche - UdeM

Nikolay Malkin

Collaborateur·rice alumni - UdeM

Cristian Dragos Manta

Doctorat - UdeM

Co-superviseur⋅e :

Dhanya Sridhar

Sören Mindermann

Collaborateur·rice de recherche - UdeM

Sarthak Mittal

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Visiteur de recherche indépendant - UdeM

Ali Parviz

Collaborateur·rice de recherche - Ying Wu Coll of Computing

Lena Podina

Doctorat - University of Waterloo

Superviseur⋅e principal⋅e :

David Rolnick

Camille Rochefort-Boulanger

Nassim Rahaman

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

Jarrid Rector-Brooks

Doctorat - UdeM

Danyal REHMAN

Postdoctorat - UdeM

James Requeima

Visiteur de recherche indépendant - UdeM

Postdoctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Julie Hussin

Victor Schmidt

Collaborateur·rice alumni - UdeM

Postdoctorat - UdeM

Maîtrise recherche - UdeM

Marcin Sendera

Collaborateur·rice alumni - UdeM

Dounia Shaaban Kabakibo

Stagiaire de recherche - UdeM

Vedant Shah

Maîtrise recherche - UdeM

Collaborateur·rice alumni

Marco Stock

Visiteur de recherche indépendant - Technical University of Munich

marco.stock@tum.de

Anja Surina

Doctorat - École Polytechnique Fédérale de Lausanne

Vincent Taboga

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Pierre-Luc Bacon

Mélisande Astrid Crystal Teng

Doctorat - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Superviseur⋅e principal⋅e :

David Rolnick

alexander.tong@mila.quebec

Alex Tong

Postdoctorat - UdeM

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Zichao Yan

Collaborateur·rice alumni - UdeM

Omar Younis

Collaborateur·rice de recherche

Co-superviseur⋅e :

Collaborateur·rice de recherche - KAIST

Doctorat - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Harry Zhao

Doctorat - McGill

Superviseur⋅e principal⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

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

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

Imagining and building wise machines: The centrality of AI metacognition

Samuel G. B. Johnson

Amir-Hossein Karimi

Nick Chater

Tobias Gerstenberg

Kate Larson

Sydney Levine

Melanie Mitchell

Iyad Rahwan

Bernhard Schölkopf

Igor Grossmann

2024-11-04

ArXiv (prépublication)

Trajectory Flow Matching with Applications to Clinical Time Series Modeling

Xi Zhang

Yuan Pu

Yuki Kawamura

Andrew Loza

Dennis Shung

Alexander Tong

Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medici… (voir plus)ne. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose Trajectory Flow Matching (TFM), which trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.

2024-10-28

ArXiv (prépublication)

Structure Language Models for Protein Conformation Generation

Jiarui Lu

Xiaoyin Chen

Stephen Zhewen Lu

Chence Shi

Hongyu Guo

Jian Tang

2024-10-24

ArXiv (prépublication)

Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases

Cristian Meo

Akihiro Nakano

Mircea Licua

Aniket Rajiv Didolkar

Masahiro Suzuki

Anirudh Goyal

Mengmi Zhang

Justin Dauwels

Yutaka Matsuo

2024-10-21

ArXiv (prépublication)

Action abstractions for amortized sampling

Oussama Boussif

Lena Nehale Ezzine

Joseph D. Viviano

Michał Koziarski

Moksh J. Jain

Nikolay Malkin

Emmanuel Bengio

Rim Assouel

2024-10-19

ArXiv (prépublication)

A Complexity-Based Theory of Compositionality

Eric Elmoznino

Thomas Jiralerspong

Guillaume Lajoie

2024-10-18

ArXiv (prépublication)

Can Safety Fine-Tuning Be More Principled? Lessons Learned from Cybersecurity

David Williams-King

Linh Le

Adam M. Oberman

As LLMs develop increasingly advanced capabilities, there is an increased need to minimize the harm that could be caused to society by certa… (voir plus)in model outputs; hence, most LLMs have safety guardrails added, for example via fine-tuning. In this paper, we argue the position that current safety fine-tuning is very similar to a traditional cat-and-mouse game (or arms race) between attackers and defenders in cybersecurity. Model jailbreaks and attacks are patched with bandaids to target the specific attack mechanism, but many similar attack vectors might remain. When defenders are not proactively coming up with principled mechanisms, it becomes very easy for attackers to sidestep any new defenses. We show how current defenses are insufficient to prevent new adversarial jailbreak attacks, reward hacking, and loss of control problems. In order to learn from past mistakes in cybersecurity, we draw analogies with historical examples and develop lessons learned that can be applied to LLM safety. These arguments support the need for new and more principled approaches to designing safe models, which are architected for security from the beginning. We describe several such approaches from the AI literature.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

Identifying and Addressing Delusions for Target-Directed Decision-Making

Harry Zhao

Mingde Zhao

Tristan Sylvain

Doina Precup

We are interested in target-directed agents, which produce targets during decision-time planning, to guide their behaviors and achieve bette… (voir plus)r generalization during evaluation. Improper training of these agents can result in delusions: the agent may come to hold false beliefs about the targets, which cannot be properly rejected, leading to unwanted behaviors and damaging out-of-distribution generalization. We identify different types of delusions by using intuitive examples in carefully controlled environments, and investigate their causes. We demonstrate how delusions can be addressed for agents trained by hindsight relabeling, a mainstream approach in for training target-directed RL agents. We validate empirically the effectiveness of the proposed solutions in correcting delusional behaviors and improving out-of-distribution generalization.

2024-10-12

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Jarrid Rector-Brooks

Mohsin Hasan

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Sarthak Mittal

Nouha Dziri

Michael M. Bronstein

Pranam Chatterjee

Alexander Tong

Joey Bose

2024-10-10

ArXiv (prépublication)

AI-Assisted Generation of Difficult Math Questions

Vedant Shah

Dingli Yu

Kaifeng Lyu

Simon Park

Jiatong Yu

Yinghui He

James Lloyd McClelland

Nan Rosemary Ke

Michael Curtis Mozer

Sanjeev Arora

Anirudh Goyal

Current LLM training positions mathematical reasoning as a core capability. With publicly available sources fully tapped, there is unmet dem… (voir plus)and for diverse and challenging math questions. Relying solely on human experts is both time-consuming and costly, while LLM-generated questions often lack the requisite diversity and difficulty. We present a design framework that combines the strengths of LLMs with a human-in-the-loop approach to generate a diverse array of challenging math questions. We leverage LLM metacognition skills [Didolkar et al., 2024] of a strong LLM to extract core"skills"from existing math datasets. These skills serve as the basis for generating novel and difficult questions by prompting the LLM with random pairs of core skills. The use of two different skills within each question makes finding such questions an"out of distribution"task for both LLMs and humans. Our pipeline employs LLMs to iteratively generate and refine questions and solutions through multiturn prompting. Human annotators then verify and further refine the questions, with their efficiency enhanced via further LLM interactions. Applying this pipeline on skills extracted from the MATH dataset [Hendrycks et al., 2021] resulted in MATH

2024-10-09

NeurIPS.cc/2024/Workshop/MATH-AI (accepté)

doi.org

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.

2024-10-09

NeurIPS.cc/2024/Workshop/Sys2-Reasoning (poster)

doi.org

Path-filtering in path-integral simulations of open quantum systems using GFlowNets

Jeremy Lackman-Mincoff

Moksh J. Jain

Nikolay Malkin