Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Founder and Scientific Advisor, Leadership Team
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning

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

Collaborating Alumni - McGill University
Collaborating Alumni - Université de Montréal
Collaborating researcher - Cambridge University
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PhD - Université de Montréal
Independent visiting researcher
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PhD - Université de Montréal
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PhD - Université de Montréal
Collaborating researcher - KAIST
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Research Intern - Université de Montréal
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PhD - Université de Montréal
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PhD - Université de Montréal
PhD - Université de Montréal
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Research Intern - Université de Montréal
PhD - Université de Montréal
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Collaborating Alumni - Université de Montréal
Research Intern - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating researcher - Université de Montréal
Collaborating Alumni - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating Alumni - Université de Montréal
Collaborating Alumni - Université de Montréal
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PhD - Université de Montréal
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Collaborating researcher - Ying Wu Coll of Computing
PhD - University of Waterloo
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Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Independent visiting researcher - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating Alumni - Université de Montréal
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Master's Research - Université de Montréal
Collaborating Alumni - Université de Montréal
Master's Research - Université de Montréal
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Independent visiting researcher - Technical University of Munich
PhD - Université de Montréal
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Postdoctorate - Université de Montréal
Postdoctorate - Université de Montréal
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Collaborating researcher - Université de Montréal
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Publications

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… (see more)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… (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.
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 … (see more)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
GFlowNet Foundations
Tristan Deleu
Edward J Hu
Salem Lahlou
Mo Tiwari
GFlowNet Foundations
Tristan Deleu
Edward J Hu
Salem Lahlou
Mo Tiwari
Discrete-Valued Neural Communication
Dianbo Liu
Alex Lamb
Kenji Kawaguchi
Anirudh Goyal
Chen Sun
Michael Curtis Mozer
Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed… (see more) of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a"cat"is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model.
Gradient Starvation: A Learning Proclivity in Neural Networks
Mohammad Pezeshki
Sékou-Oumar Kaba
We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks… (see more). Gradient Starvation arises when cross-entropy loss is minimized by capturing only a subset of features relevant for the task, despite the presence of other predictive features that fail to be discovered. This work provides a theoretical explanation for the emergence of such feature imbalance in neural networks. Using tools from Dynamical Systems theory, we identify simple properties of learning dynamics during gradient descent that lead to this imbalance, and prove that such a situation can be expected given certain statistical structure in training data. Based on our proposed formalism, we develop guarantees for a novel regularization method aimed at decoupling feature learning dynamics, improving accuracy and robustness in cases hindered by gradient starvation. We illustrate our findings with simple and real-world out-of-distribution (OOD) generalization experiments.
Neural Production Systems
Anirudh Goyal
Aniket Rajiv Didolkar
Nan Rosemary Ke
Charles Blundell
Philippe Beaudoin
Nicolas Heess
Michael Curtis Mozer
Visual environments are structured, consisting of distinct objects or entities. These entities have properties---visible or latent---that d… (see more)etermine the manner in which they interact with one another. To partition images into entities, deep-learning researchers have proposed structural inductive biases such as slot-based architectures. To model interactions among entities, equivariant graph neural nets (GNNs) are used, but these are not particularly well suited to the task for two reasons. First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be. Second, GNNs do not factorize knowledge about interactions in an entity-conditional manner. As an alternative, we take inspiration from cognitive science and resurrect a classic approach, production systems, which consist of a set of rule templates that are applied by binding placeholder variables in the rules to specific entities. Rules are scored on their match to entities, and the best fitting rules are applied to update entity properties. In a series of experiments, we demonstrate that this architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information. This disentangling of knowledge achieves robust future-state prediction in rich visual environments, outperforming state-of-the-art methods using GNNs, and allows for the extrapolation from simple (few object) environments to more complex environments.
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
Kevin Muyuan Xia
Kai-Zhan Lee
Elias Bareinboim
One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mech… (see more)anisms and exogenous sources of random variation of the system under investigation (Pearl, 2000). An important property of many kinds of neural networks is universal approximability: the ability to approximate any function to arbitrary precision. Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM. In this paper, we show this is not the case by disentangling the notions of expressivity and learnability. Specifically, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020), which describes the limits of what can be learned from data, still holds for neural models. For instance, an arbitrarily complex and expressive neural net is unable to predict the effects of interventions given observational data alone. Given this result, we introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences. Building on this new class of models, we focus on solving two canonical tasks found in the literature known as causal identification and estimation. Leveraging the neural toolbox, we develop an algorithm that is both sufficient and necessary to determine whether a causal effect can be learned from data (i.e., causal identifiability); it then estimates the effect whenever identifiability holds (causal estimation). Simulations corroborate the proposed approach.
Problèmes associés au déploiement des modèles fondés sur l’apprentissage machine en santé
Joseph Paul Cohen
Tianshi Cao
Joseph D Viviano
Chin-Wei Huang
Michael Fralick
Marzyeh Ghassemi
Muhammad Mamdani
Russell Greiner
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy
Ingmar Posner
T. Barfoot
Philippe Beaudoin
Jeannette Bohg
Oliver Brock
Isabelle Depatie
Dieter Fox
D. Koditschek
Tom'as Lozano-p'erez
Vikash K. Mansinghka
Dorsa Sadigh
Stefan Schaal
G. Sukhatme
Denis Therien
Marc Emile Toussaint
Michiel van de Panne