Portrait de Yoshua Bengio

Yoshua Bengio

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur titulaire, Université de Montréal, Département d'informatique et de recherche opérationnelle
Fondateur et Conseiller scientifique, Équipe de direction
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Modélisation moléculaire
Neurosciences computationnelles
Raisonnement
Réseaux de neurones en graphes
Réseaux de neurones récurrents
Théorie de l'apprentissage automatique
Traitement du langage naturel

Biographie

*Pour toute demande média, veuillez écrire à medias@mila.quebec.

Pour plus d’information, contactez Marie-Josée Beauchamp, adjointe administrative à marie-josee.beauchamp@mila.quebec.

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

Collaborateur·rice alumni - McGill
Collaborateur·rice alumni - UdeM
Collaborateur·rice de recherche - Cambridge University
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Visiteur de recherche indépendant - KAIST
Visiteur de recherche indépendant
Co-superviseur⋅e :
Doctorat - UdeM
Collaborateur·rice de recherche - N/A
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Collaborateur·rice de recherche - KAIST
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Stagiaire de recherche - UdeM
Doctorat - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - UdeM
Collaborateur·rice alumni - UdeM
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni
Doctorat - UdeM
Collaborateur·rice alumni - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Collaborateur·rice de recherche - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - Ying Wu Coll of Computing
Doctorat - University of Waterloo
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - Max-Planck-Institute for Intelligent Systems
Stagiaire de recherche - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Postdoctorat - UdeM
Visiteur de recherche indépendant - UdeM
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice alumni - UdeM
Maîtrise recherche - UdeM
Collaborateur·rice alumni - UdeM
Maîtrise recherche - UdeM
Visiteur de recherche indépendant - Technical University of Munich
Doctorat - UdeM
Co-superviseur⋅e :
Postdoctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - UdeM
Collaborateur·rice de recherche
Collaborateur·rice de recherche - KAIST
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :

Publications

Neural Attentive Circuits
Nasim Rahaman
Martin Weiss
Francesco Locatello
Bernhard Schölkopf
Li Erran Li
Nicolas Ballas
Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modali… (voir plus)ties. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural architectures that represent the data using sparsely interacting modules. These models can be more robust out-of-distribution, computationally efficient, and capable of sample-efficient adaptation to new data. However, they tend to make domain-specific assumptions about the data, and present challenges in how module behavior (i.e., parameterization) and connectivity (i.e., their layout) can be jointly learned. In this work, we introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) that jointly learns the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs are best understood as the combination of two systems that are jointly trained end-to-end: one that determines the module configuration and the other that executes it on an input. We demonstrate qualitatively that NACs learn diverse and meaningful module configurations on the NLVR2 dataset without additional supervision. Quantitatively, we show that by incorporating modularity in this way, NACs improve upon a strong non-modular baseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about 10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that NACs can achieve an 8x speedup at inference time while losing less than 3% performance. Finally, we find NACs to yield competitive results on diverse data modalities spanning point-cloud classification, symbolic processing and text-classification from ASCII bytes, thereby confirming its general purpose nature.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Eric Larsen
Sébastien Lachapelle
Andrea Lodi
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a method… (voir plus)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.
TaHiD: Tackling Data Hiding in Fake News Detection with News Propagation Networks
Adrien Benamira
Benjamin Devillers
Etienne Lesot
Ayush K. Ray
Manal Saadi
Fragkiskos D 587
Steven Bird
Ewan Klein
Edward Loper
Nat-593
Carlos Castillo
Marcelo Mendoza
Barbara Poblete
Daryna Dementieva
Alexander Panchenko
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Ashish Vaswani
Noam M. Shazeer … (voir 8 de plus)
Niki Parmar
Pietro Lio’
Yaqing Wang
Fenglong Ma
Zhiwei Jin
Ye Yuan
Fake news with detrimental societal effects has 001 attracted extensive attention and research. De-002 spite early success, the state-of-the… (voir plus)-art meth-003 ods fall short of considering the propagation 004 of news. News propagates at different times 005 through different mediums, including users, 006 comments, and sources, which form the news 007 propagation network. Moreover, the serious 008 problem of data hiding arises, which means 009 that fake news publishers disguise fake news 010 as real to confuse users by deleting comments 011 that refute the rumor or deleting the news itself 012 when it has been spread widely. Existing meth-013 ods do not consider the propagation of news 014 and fail to identify what matters in the process, 015 which leads to fake news hiding in the prop-016 agation network and escaping from detection. 017 Inspired by the propagation of news, we pro-018 pose a novel fake news detection framework 019 named TaHiD, which models the propagation 020 as a heterogeneous dynamic graph and contains 021 the propagation attention module to measure 022 the influence of different propagation. Exper-023 iments demonstrate that TaHiD extracts use-024 ful information from the news propagation net-025 work and outperforms state-of-the-art methods 026 on several benchmark datasets for fake news 027 detection. Additional studies also show that 028 TaHiD is capable of identifying fake news in 029 the case of data hiding. 030
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Aniket Rajiv Didolkar
Kshitij Gupta
Anirudh Goyal
Nitesh Bharadwaj Gundavarapu
Alex Lamb
Nan Rosemary Ke
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Anthony Zador
Bence Ölveczky
Sean Escola
Kwabena Boahen
Matthew Botvinick
Dmitri Chklovskii
Anne Churchland
Claudia Clopath
James DiCarlo
Surya Ganguli
Jeff Hawkins
Konrad Paul Kording
Alexei Koulakov
Yann LeCun
Timothy P. Lillicrap
Adam Marblestone
Bruno Olshausen
Alexandre Pouget … (voir 7 de plus)
Cristina Savin
Terrence Sejnowski
Eero Simoncelli
Sara Solla
David Sussillo
Andreas S. Tolias
Doris Tsao
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Anthony Zador
Bence Ölveczky
Sean Escola
Kwabena Boahen
Matthew Botvinick
Dmitri Chklovskii
Anne Churchland
Claudia Clopath
James DiCarlo
Surya Ganguli
Jeff Hawkins
Konrad Paul Kording
Alexei Koulakov
Yann LeCun
Timothy P. Lillicrap
Adam Marblestone
Bruno Olshausen
Alexandre Pouget … (voir 7 de plus)
Cristina Savin
Terrence Sejnowski
Eero Simoncelli
Sara Solla
David Sussillo
Andreas S. Tolias
Doris Tsao
Trajectory Balance: Improved Credit Assignment in GFlowNets
Nikolay Malkin
Moksh J. Jain
Chen Sun
Generative flow networks (GFlowNets) are a method for learning a stochastic policy for generating compositional objects, such as graphs or s… (voir plus)trings, from a given unnormalized density by sequences of actions, where many possible action sequences may lead to the same object. We find previously proposed learning objectives for GFlowNets, flow matching and detailed balance, which are analogous to temporal difference learning, to be prone to inefficient credit propagation across long action sequences. We thus propose a new learning objective for GFlowNets, trajectory balance, as a more efficient alternative to previously used objectives. We prove that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution. In experiments on four distinct domains, we empirically demonstrate the benefits of the trajectory balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequences and large action spaces.
Unifying Likelihood-free Inference with Black-box Optimization and Beyond
Dinghuai Zhang
Jie Fu
Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on th… (voir plus)e pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous optimization approaches can be"reinvented"in our framework, and further propose new probabilistic black-box optimization algorithms. Extensive experiments on sequence design application illustrate the benefits of the proposed methodology.
Weakly Supervised Representation Learning with Sparse Perturbations
Kartik Ahuja
Jason Hartford
The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge o… (voir plus)r any source of supervision. Most prior approaches require strong distributional assumptions on the latent variables and weak supervision (auxiliary information such as timestamps) to provide provable identification guarantees. In this work, we show that if one has weak supervision from observations generated by sparse perturbations of the latent variables--e.g. images in a reinforcement learning environment where actions move individual sprites--identification is achievable under unknown continuous latent distributions. We show that if the perturbations are applied only on mutually exclusive blocks of latents, we identify the latents up to those blocks. We also show that if these perturbation blocks overlap, we identify latents up to the smallest blocks shared across perturbations. Consequently, if there are blocks that intersect in one latent variable only, then such latents are identified up to permutation and scaling. We propose a natural estimation procedure based on this theory and illustrate it on low-dimensional synthetic and image-based experiments.
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
Enoch Amoatey Tetteh
Joseph D Viviano
Joseph Paul Cohen
Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (voir plus)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.
Effect of diversity in Meta-Learning
Ramnath Kumar
Tristan Deleu
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task … (voir plus)distribution plays a vital role in the performance of the model. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; we study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms. For this experiment, we train on two datasets - Omniglot and miniImageNet and with three broad classes of meta-learning models - Metric-based (i.e., Protonet, Matching Networks), Optimization-based (i.e., MAML, Reptile, and MetaOptNet), and Bayesian meta-learning models (i.e., CNAPs). Our experiments demonstrate that the effect of task diversity on all these algorithms follows a similar trend, and task diversity does not seem to offer any benefits to the learning of the model. Furthermore, we also demonstrate that even a handful of tasks, repeated over multiple batches, would be sufficient to achieve a performance similar to uniform sampling and draws into question the need for additional tasks to create better models.
GFlowNet Foundations
Tristan Deleu
Edward J Hu
Salem Lahlou
Mo Tiwari
Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, w… (voir plus)ith a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal distributions where some variables are unspecified and, of particular interest, can represent distributions over composite objects like sets and graphs. GFlowNets amortize the work typically done by computationally expensive MCMC methods in a single but trained generative pass. They could also be used to estimate partition functions and free energies, conditional probabilities of supersets (supergraphs) given a subset (subgraph), as well as marginal distributions over all supersets (supergraphs) of a given set (graph). We introduce variations enabling the estimation of entropy and mutual information, sampling from a Pareto frontier, connections to reward-maximizing policies, and extensions to stochastic environments, continuous actions and modular energy functions.