Yoshua Bengio

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 director 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 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

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Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Harry Zhao

PhD - McGill University

Principal supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

Constant Memory Attentive Neural Processes

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

2023-01-01

arXiv.org (preprint)

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks

Lazar Atanackovic

Alexander Tong

Jason Hartford

Leo Jingyu Lee

Bo Wang

Learning the causal structure of observable variables is a central focus for scientiﬁc discovery. Bayesian causal discovery methods tackle… (see more) this problem by learning a posterior over the set of admissible graphs given our priors and observations. Existing methods primarily consider observations from static systems and assume the underlying causal structure takes the form of a directed acyclic graph (DAG). In settings with dynamic feedback mechanisms that regulate the trajectories of individual variables, this acyclicity assumption fails unless we account for time. We focus on learning Bayesian posteriors over cyclic graphs and treat causal discovery as a problem of sparse identiﬁcation of a dynamical sys-tem. This imposes a natural temporal causal order between variables and captures cyclic feedback loops through time. Under this lens, we propose a new framework for Bayesian causal discovery for dynamical systems and present a novel generative ﬂow network architecture (DynGFN) tailored for this task. Our results indicate that DynGFN learns posteriors that better encapsulate the distributions over admissible cyclic causal structures compared to counterpart state-of-the-art approaches.

2023-01-01

arXiv.org (preprint)

GFlowNets for AI-Driven Scientific Discovery

Moksh J. Jain

Tristan Deleu

Jason Hartford

Cheng-Hao Liu

Alex Hernandez-Garcia

Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (see more)ace of scientific discovery. While science has traditionally relied...

2023-01-01

Digital Discovery (published)

arxiv.org

GFlowOut: Dropout with Generative Flow Networks

Dianbo Liu

Moksh J. Jain

Bonaventure F. P. Dossou

Qianli Shen

Salem Lahlou

Anirudh Goyal

Nikolay Malkin

Chris Emezue

Dinghuai Zhang

Nadhir Hassen

Xu Ji

Kenji Kawaguchi

2023-01-01

ICML (published)

GFlowOut: Dropout with Generative Flow Networks

Dianbo Liu

Moksh J. Jain

Bonaventure F. P. Dossou

Qianli Shen

Salem Lahlou

Anirudh Goyal

Nikolay Malkin

Chris Emezue

Dinghuai Zhang

Nadhir Hassen

Xu Ji

Kenji Kawaguchi

2023-01-01

ICML (published)

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

Eric Nguyen

Michael Poli

Marjan Faizi

Armin W Thomas

Callum Birch-Sykes

Michael Wornow

Aman Patel

Clayton M. Rabideau

Stefano Massaroli

Stefano Ermon

Stephen Baccus

Christopher Re

Learning GFlowNets from partial episodes for improved convergence and stability

Kanika Madan

Jarrid Rector-Brooks

Maksym Korablyov

Emmanuel Bengio

Moksh J. Jain

Andrei Cristian Nica

Tom Bosc

Nikolay Malkin

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized … (see more)target density and have been successfully used for various probabilistic modeling tasks. Existing training objectives for GFlowNets are either local to states or transitions, or propagate a reward signal over an entire sampling trajectory. We argue that these alternatives represent opposite ends of a gradient bias-variance tradeoff and propose a way to exploit this tradeoff to mitigate its harmful effects. Inspired by the TD(

2023-01-01

ICML (published)

MixupE: Understanding and Improving Mixup from Directional Derivative Perspective

Vikas Verma

Yingtian Zou

Sarthak Mittal

Wai Hoh Tang

Hieu Pham

Juho Kannala

Arno Solin

Kenji Kawaguchi

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpol… (see more)ating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across multiple datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.

2023-01-01

UAI (published)

MixupE: Understanding and improving Mixup from directional derivative perspective

Vikas Verma

Yingtian Zou

Sarthak Mittal

Wai Hoh Tang

Hieu Pham

Juho Kannala

Arno Solin

Kenji Kawaguchi

2023-01-01

UAI (published)

NEURAL NETWORK-BASED SOLVERS FOR PDES

M. Cameron

Ian G Goodfellow

(1) N (x; θ) = Ll+1 ○ σl ○Ll ○ σl−1 ○ . . . ○ σ1 ○L1. The symbol Lk denotes the k’s affine operator of the form Lk(x) = … (see more)Akx + bk, while σk denotes a nonlinear function called an activation function. The activation functions are chosen by the user. The matrices Ak and shift vectors (or bias vectors) bk are encoded into the argument θ: θ = {Ak, bk} l+1 k=1. The term training neural network means finding {Ak, bk} l+1 k=1 such that N (x; θ) satisfies certain conditions. These conditions are described by the loss function chosen by the user. For example, one might want the neural network to assume certain values fj at certain points xj , j = 1, . . . ,N . These points x are called the training data. In this case, a common choice of the loss function is the least squares error:

2023-01-01

(published)

www.semanticscholar.org

Stochastic Generative Flow Networks

Ling Pan

Dinghuai Zhang

Moksh J. Jain

Longbo Huang

2023-01-01

UAI (published)

Stochastic Generative Flow Networks

Ling Pan

Dinghuai Zhang

Moksh J. Jain

Longbo Huang

Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures… (see more) through the lens of ``inference as control''. They have shown great potential in generating high-quality and diverse candidates from a given energy landscape. However, existing GFlowNets can be applied only to deterministic environments, and fail in more general tasks with stochastic dynamics, which can limit their applicability. To overcome this challenge, this paper introduces Stochastic GFlowNets, a new algorithm that extends GFlowNets to stochastic environments. By decomposing state transitions into two steps, Stochastic GFlowNets isolate environmental stochasticity and learn a dynamics model to capture it. Extensive experimental results demonstrate that Stochastic GFlowNets offer significant advantages over standard GFlowNets as well as MCMC- and RL-based approaches, on a variety of standard benchmarks with stochastic dynamics.

2023-01-01

UAI (published)