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
Website
Google Scholar

Biography

*For media requests, please write to medias@mila.quebec.

For more information please contact Marie-Josée Beauchamp, Administrative Assistant at marie-josee.beauchamp@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 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

Jamal Abou Haibeh
Collaborating Alumni - McGill University
Github
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Mohammed Abukalam
Collaborating Alumni - Université de Montréal
Github
Berkes Anaïs
Collaborating researcher - Cambridge University
Principal supervisor :
David Rolnick
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Github
Google Scholar
Rim Assouel
PhD - Université de Montréal
Stefan Bauer
Independent visiting researcher
Co-supervisor :
Guillaume Lajoie
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Paul Bertin
PhD - Université de Montréal
Joyce Chai
Independent visiting researcher
Principal supervisor :
Siva Reddy
Shahana Chatterjee
Collaborating researcher - N/A
Principal supervisor :
David Rolnick
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Xiaoyin Chen
PhD - Université de Montréal
Sanghyeok Choi
Collaborating researcher - KAIST
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Tristan Deleu
Collaborating Alumni - Université de Montréal
Website
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Aniket Didolkar
PhD - Université de Montréal
Website
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Abdessamad EL KABID
Collaborating Alumni - Université de Montréal
Co-supervisor :
Loubna Benabbou
Desmond Elliott
Independent visiting researcher
Principal supervisor :
Siva Reddy
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Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
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Github
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Léna Ezzine
PhD - Université de Montréal
Jean-Pierre Falet
PhD - Université de Montréal
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Leo Feng
PhD - Université de Montréal
Website
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Pietro Greiner
PhD
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Mohsin Hasan
PhD - Université de Montréal
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Github
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Edward Hu
PhD - Université de Montréal
Moksh Jain
PhD - Université de Montréal
Website
Github
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Thomas Jiralerspong
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
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Younesse Kaddar
Collaborating Alumni - Université de Montréal
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Github
Hyeonah Kim
Postdoctorate - Université de Montréal
Principal supervisor :
Alex Hernandez-Garcia
Salem Lahlou
Collaborating Alumni - Université de Montréal
Tabitha Edith Lee
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Github
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Zhen Liu
Collaborating Alumni - Université de Montréal
Principal supervisor :
Liam Paull
Chenghao Liu
Collaborating Alumni
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Kanika Madan
PhD - Université de Montréal
Nikolay Malkin
Collaborating Alumni - Université de Montréal
Website
Github
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Cristian Dragos Manta
PhD - Université de Montréal
Co-supervisor :
Dhanya Sridhar
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Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
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Jama Mohamud
PhD - Université de Montréal
Principal supervisor :
Mirco Ravanelli
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Cyrus Neary
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Github
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Phong Nguyen
Independent visiting researcher - Université de Montréal
Padideh Nouri
PhD - Université de Montréal
Principal supervisor :
Doina Precup
Website
Github
Google Scholar
Ali Parviz
Collaborating researcher - Ying Wu Coll of Computing
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Lena Podina
Collaborating researcher - University of Waterloo
Principal supervisor :
David Rolnick
Google Scholar
Nassim Rahaman
Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Amine RAZIG
Collaborating researcher - Université de Montréal
Co-supervisor :
Loubna Benabbou
Website
Jarrid Rector-Brooks
PhD - Université de Montréal
Danyal REHMAN
Postdoctorate - Université de Montréal
Github
James Requeima
Independent visiting researcher - Université de Montréal
Oli RICHARDSON
Postdoctorate - Université de Montréal
Website
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Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
Github
Abhik Roychoudhury Roychoudhury
Independent visiting researcher
Principal supervisor :
Siva Reddy
Github
Luca Scimeca
Postdoctorate - Université de Montréal
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Google Scholar
Dragos Secrieru
Collaborating Alumni - Université de Montréal
Marcin Sendera
Collaborating Alumni - Université de Montréal
Github
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Divya Sharma
Postdoctorate
Co-supervisor :
Alex Hernandez-Garcia
Website
Github
Marco Stock
Independent visiting researcher - Technical University of Munich
Github
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
Hugo Larochelle
Website
Github
Ivan Titov
Independent visiting researcher
Principal supervisor :
Siva Reddy
Website
Google Scholar
Alex Tong
Collaborating Alumni - Université de Montréal
Website
Github
Google Scholar
Donna Vakalis
Postdoctorate - Université de Montréal
Co-supervisor :
David Rolnick
Website
Github
Google Scholar
Siddarth Venkatraman
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
Website
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Omar G. Younis
Collaborating researcher
Website
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Taeyoung YUN
Collaborating researcher - Université de Montréal
Github
Tianyu Zhang
PhD - Université de Montréal
Website
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Nicole Zhang
PhD - McGill University
Principal supervisor :
Mathieu Blanchette
Website
Github
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Dinghuai Zhang
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
Website
Harry Zhao
Collaborating Alumni - McGill University
Principal supervisor :
Doina Precup
Website
Github
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Publications

Simulation-Free Schrödinger Bridges via Score and Flow Matching
Alexander Tong
Nikolay Malkin
Kilian Fatras
Lazar Atanackovic
Yanlei Zhang
Guillaume Huguet
Guy Wolf
Yoshua Bengio
We present simulation-free score and flow matching ([SF]…
2024-01-01
AISTATS (published)
doi.org
openreview.net
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’
Yoshua Bengio
Michael M. Bronstein
2023-12-12
ArXiv (preprint)
doi.org
arxiv.org
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’
Yoshua Bengio
Michael M. Bronstein
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graph… (see more)s with atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations. In recent years, Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. Their specificity lies in the inductive biases they leverage - such as physical symmetries and chemical properties - to learn informative representations of these geometric graphs. In this opinionated paper, we provide a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems. We cover fundamental background material and introduce a pedagogical taxonomy of Geometric GNN architectures: (1) invariant networks, (2) equivariant networks in Cartesian basis, (3) equivariant networks in spherical basis, and (4) unconstrained networks. Additionally, we outline key datasets and application areas and suggest future research directions. The objective of this work is to present a structured perspective on the field, making it accessible to newcomers and aiding practitioners in gaining an intuition for its mathematical abstractions.
2023-12-12
ArXiv (preprint)
doi.org
arxiv.org
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’
Yoshua Bengio
Michael M. Bronstein
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graph… (see more)s with atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations. In recent years, Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. Their specificity lies in the inductive biases they leverage - such as physical symmetries and chemical properties - to learn informative representations of these geometric graphs. In this opinionated paper, we provide a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems. We cover fundamental background material and introduce a pedagogical taxonomy of Geometric GNN architectures: (1) invariant networks, (2) equivariant networks in Cartesian basis, (3) equivariant networks in spherical basis, and (4) unconstrained networks. Additionally, we outline key datasets and application areas and suggest future research directions. The objective of this work is to present a structured perspective on the field, making it accessible to newcomers and aiding practitioners in gaining an intuition for its mathematical abstractions.
2023-12-12
ArXiv (preprint)
doi.org
arxiv.org
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’
Yoshua Bengio
Michael M. Bronstein
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graph… (see more)s with atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations. In recent years, Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. Their specificity lies in the inductive biases they leverage - such as physical symmetries and chemical properties - to learn informative representations of these geometric graphs. In this opinionated paper, we provide a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems. We cover fundamental background material and introduce a pedagogical taxonomy of Geometric GNN architectures: (1) invariant networks, (2) equivariant networks in Cartesian basis, (3) equivariant networks in spherical basis, and (4) unconstrained networks. Additionally, we outline key datasets and application areas and suggest future research directions. The objective of this work is to present a structured perspective on the field, making it accessible to newcomers and aiding practitioners in gaining an intuition for its mathematical abstractions.
2023-12-12
ArXiv (preprint)
doi.org
arxiv.org
Improving Gradient-guided Nested Sampling for Posterior Inference
Pablo Lemos
Will Handley
Nikolay Malkin
Yoshua Bengio
Yashar Hezaveh
Laurence Perreault-Levasseur
We present a performant, general-purpose gradient-guided nested sampling algorithm, …
2023-12-06
ArXiv (preprint)
doi.org
openreview.net
Unlearning via Sparse Representations
Vedant Shah
Frederik Träuble
Ashish Malik
Hugo Larochelle
Michael Curtis Mozer
Sanjeev Arora
Yoshua Bengio
Anirudh Goyal
2023-11-26
ArXiv (preprint)
doi.org
openreview.net
Unlearning via Sparse Representations
Vedant Shah
Frederik Träuble
Ashish Malik
Hugo Larochelle
Michael Curtis Mozer
Sanjeev Arora
Yoshua Bengio
Anirudh Goyal
Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infea… (see more)sible by existing techniques. We propose a nearly compute-free zero-shot 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 data set. We evaluate the proposed technique on the problem of \textit{class unlearning} using three datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all three datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.
2023-11-26
ArXiv (preprint)
doi.org
arxiv.org
Mitigating Biases with Diverse Ensembles and Diffusion Models
Luca Scimeca
Alexander Rubinstein
Damien Teney
Seong Joon Oh
Armand Mihai Nicolicioiu
Yoshua Bengio
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (see more)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose an ensemble diversification framework exploiting Diffusion Probabilistic Models (DPMs) to mitigate this form of bias. We show that at particular training intervals, DPMs can generate images with novel feature combinations, even when trained on samples displaying correlated input features. We leverage this crucial property to generate synthetic counterfactuals to increase model diversity via ensemble disagreement. We show that DPM-guided diversification is sufficient to remove dependence on primary shortcut cues, without a need for additional supervised signals. We further empirically quantify its efficacy on several diversification objectives, and finally show improved generalization and diversification performance on par with prior work that relies on auxiliary data collection.
2023-11-23
ArXiv (preprint)
arxiv.org
arxiv.org
Learning from unexpected events in the neocortical microcircuit
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 … (see 3 more)
Timothy P. Lillicrap
Blake Richards
Joel Zylberberg
2023-11-21
Journal of Neuroscience (published)
doi.org
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 … (see 3 more)
Timothy P. Lillicrap
Blake Richards
Joel Zylberberg
Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In… (see more) 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.
2023-11-21
Journal of Neuroscience (published)
doi.org
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 … (see 3 more)
Timothy P. Lillicrap
Blake Richards
Joel Zylberberg
Scientists have long conjectured that the neocortex learns patterns in sensory data to generate top-down predictions of upcoming stimuli. In… (see more) 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.
2023-11-21
Journal of Neuroscience (published)
doi.org