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
Website
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
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
Website
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Tristan Deleu
PhD - Université de Montréal
Website
Github
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Aniket Didolkar
PhD - Université de Montréal
Website
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Abdessamad EL KABID
Research Intern - Université de Montréal
Co-supervisor :
Loubna Benabbou
Github
Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
Website
Github
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Léna Ezzine
PhD - Université de Montréal
Github
Jean-Pierre Falet
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
Website
Github
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Leo Feng
PhD - Université de Montréal
leo.feng@mila.quebec
Website
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Ivan Grega
Research Intern - Université de Montréal
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Pietro Greiner
PhD
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Mohsin Hasan
PhD - Université de Montréal
mohsin.hasan@mila.quebec
Website
Github
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Edward Hu
PhD - Université de Montréal
Moksh Jain
PhD - Université de Montréal
moksh.jain@mila.quebec
Website
Github
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Thomas Jiralerspong
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
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Hyeonah Kim
Postdoctorate - Université de Montréal
Principal supervisor :
Alex Hernandez
Minsu Kim
Research Intern - Université de Montréal
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Github
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Yaroslav KIVVA
Collaborating researcher - Université de Montréal
Github
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Salem Lahlou
Collaborating Alumni - Université de Montréal
Github
Tabitha Edith Lee
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
Website
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Seanie Lee
Collaborating Alumni - Université de Montréal
Website
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Chenghao Liu
Collaborating Alumni
Website
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Zhen Liu
Collaborating Alumni - Université de Montréal
Principal supervisor :
Liam Paull
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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Sören Mindermann
Collaborating researcher - Université de Montréal
Website
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Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
Github
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Jama Mohamud
PhD - Université de Montréal
Principal supervisor :
Mirco Ravanelli
Website
Github
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Cyrus Neary
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Github
Google Scholar
Phong Nguyen
Independent visiting researcher - Université de Montréal
Padideh Nouri
PhD - Université de Montréal
Principal supervisor :
Doina Precup
Website
Github
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Ali Parviz
Collaborating researcher - Ying Wu Coll of Computing
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Lena Podina
PhD - University of Waterloo
Principal supervisor :
David Rolnick
Github
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Nassim Rahaman
Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
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
Github
Google Scholar
Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
Github
Victor Schmidt
Collaborating Alumni - Université de Montréal
Website
Github
Google Scholar
Luca Scimeca
Postdoctorate - Université de Montréal
Website
Github
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Dragos Secrieru
Master's Research - Université de Montréal
Marcin Sendera
Collaborating Alumni - Université de Montréal
Github
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Vedant Shah
Master's Research - Université de Montréal
Website
Github
Google Scholar
Divya Sharma
Postdoctorate
Website
Github
Marco Stock
Independent visiting researcher - Technical University of Munich
marco.stock@tum.de
Github
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
Hugo Larochelle
Website
Github
Alex Tong
Postdoctorate - Université de Montréal
alexander.tong@mila.quebec
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
Github
Google Scholar
Leah WAIRIMU
Collaborating researcher - Université de Montréal
Github
Omar G. Younis
Collaborating researcher
Website
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Taeyoung YUN
Collaborating researcher - KAIST
Github
Tianyu Zhang
PhD - Université de Montréal
Website
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Nicole Zhang
PhD - McGill University
Principal supervisor :
Mathieu Blanchette
Github
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Dinghuai Zhang
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
Website
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Harry Zhao
PhD - McGill University
Principal supervisor :
Doina Precup
Website
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Publications

GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
2023-06-06
ArXiv (preprint)
doi.org
arxiv.org
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
2023-06-06
ArXiv (preprint)
doi.org
arxiv.org
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste
Nils Lehmann
Pau Rodriguez
Evan David Sherwin
Hannah Kerner
Björn Lütjens
Jeremy Andrew Irvin
David Dao
Hamed Alemohammad
Alexandre Drouin
Mehmet Gunturkun
Gabriel Huang
David Vazquez
Dava Newman
Yoshua Bengio
Stefano Ermon
Xiao Xiang Zhu
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (see more)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.
2023-06-06
ArXiv (preprint)
doi.org
arxiv.org
Cycle Consistency Driven Object Discovery
Aniket Rajiv Didolkar
Anirudh Goyal
Yoshua Bengio
Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (see more)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
2023-06-03
ArXiv (preprint)
doi.org
arxiv.org
Cycle Consistency Driven Object Discovery
Aniket Rajiv Didolkar
Anirudh Goyal
Yoshua Bengio
Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (see more)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
2023-06-03
ArXiv (preprint)
doi.org
arxiv.org
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush K Chakravarthy
Trang M. Nguyen
Anirudh Goyal
Yoshua Bengio
Michael Curtis Mozer
2023-05-31
ArXiv (preprint)
doi.org
arxiv.org
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush K Chakravarthy
Trang M. Nguyen
Anirudh Goyal
Yoshua Bengio
Michael Curtis Mozer
The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a… (see more) set of interchangeable modules called \emph{slots} or \emph{object files} that compete for local patches of an image. The competition has a weak inductive bias to preserve spatial continuity; consequently, one slot may claim patches scattered diffusely throughout the image. In contrast, the inductive bias of human vision is strong, to the degree that attention has classically been described with a spotlight metaphor. We incorporate a spatial-locality prior into state-of-the-art object-centric vision models and obtain significant improvements in segmenting objects in both synthetic and real-world datasets. Similar to human visual attention, the combination of image content and spatial constraints yield robust unsupervised object-centric learning, including less sensitivity to model hyperparameters.
2023-05-31
ArXiv (preprint)
doi.org
arxiv.org
Attention Schema in Neural Agents
Dianbo Liu
Samuele Bolotta
Mike He Zhu
Yoshua Bengio
Guillaume Dumas
Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static s… (see more)election of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.
2023-05-27
ArXiv (preprint)
doi.org
arxiv.org
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Aaron Courville
Yoshua Bengio
Ling Pan
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (see more)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.
2023-05-26
ArXiv (preprint)
doi.org
arxiv.org
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Yoshua Bengio
Paul F. Christiano … (see 1 more)
Allan Dafoe
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
2023-05-24
ArXiv (preprint)
doi.org
arxiv.org
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Yoshua Bengio
Paul F. Christiano … (see 1 more)
Allan Dafoe
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
2023-05-24
ArXiv (preprint)
doi.org
arxiv.org
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Yoshua Bengio
Paul F. Christiano … (see 1 more)
Allan Dafoe
2023-05-24
ArXiv (preprint)
doi.org
arxiv.org