Yoshua Bengio

Biography

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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

Mohammed Abukalam

Collaborating Alumni - Université de Montréal

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

Rim Assouel

PhD - Université de Montréal

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Paul Bertin

PhD - Université de Montréal

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

David Rolnick

Xiaoyin Chen

PhD - Université de Montréal

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

PhD - Université de Montréal

Research Intern - Université de Montréal

Co-supervisor :

Loubna Benabbou

Eric Elmoznino

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

Co-supervisor :

Leo Feng

PhD - Université de Montréal

leo.feng@mila.quebec

Ivan Grega

Research Intern - Université de Montréal

PhD

PhD - Université de Montréal

mohsin.hasan@mila.quebec

Edward Hu

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Minsu Kim

Research Intern - Université de Montréal

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernandez

Yaroslav KIVVA

Collaborating researcher - Université de Montréal

Salem Lahlou

Collaborating Alumni - Université de Montréal

Tabitha Edith Lee

Postdoctorate - Université de Montréal

Principal supervisor :

Seanie Lee

Collaborating Alumni - Université de Montréal

Collaborating Alumni

Zhen Liu

Collaborating Alumni - Université de Montréal

Principal supervisor :

Liam Paull

Kanika Madan

PhD - Université de Montréal

Nikolay Malkin

Collaborating Alumni - Université de Montréal

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Sören Mindermann

Collaborating researcher - Université de Montréal

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Principal supervisor :

Independent visiting researcher - Université de Montréal

Padideh Nouri

PhD - Université de Montréal

Principal supervisor :

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

Lena Podina

PhD - University of Waterloo

Principal supervisor :

Collaborating Alumni - Max-Planck-Institute for Intelligent Systems

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

James Requeima

Independent visiting researcher - Université de Montréal

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Julie Hussin

Victor Schmidt

Collaborating Alumni - Université de Montréal

Postdoctorate - Université de Montréal

Master's Research - Université de Montréal

Marcin Sendera

Collaborating Alumni - Université de Montréal

Vedant Shah

Master's Research - Université de Montréal

Postdoctorate

Marco Stock

Independent visiting researcher - Technical University of Munich

marco.stock@tum.de

Mélisande Astrid Crystal Teng

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

alexander.tong@mila.quebec

Alex Tong

Postdoctorate - Université de Montréal

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Omar G. Younis

Collaborating researcher

Collaborating researcher - KAIST

Nicole Zhang

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

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

Improved off-policy training of diffusion samplers

Marcin Sendera

Minsu Kim

Sarthak Mittal

Pablo Lemos

Luca Scimeca

Jarrid Rector-Brooks

Alexandre Adam

Nikolay Malkin

We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (see more)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at [this link](https://github.com/GFNOrg/gfn-diffusion) as a base for future work on diffusion models for amortized inference.

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving

Aniket Rajiv Didolkar

Anirudh Goyal

Nan Rosemary Ke

Siyuan Guo

Michal Valko

Timothy P Lillicrap

Danilo Jimenez Rezende

Michael Curtis Mozer

Sanjeev Arora

2024-09-25

NeurIPS.cc/2024/Conference (poster)

RGFN: Synthesizable Molecular Generation Using GFlowNets

Michał Koziarski

Andrei Rekesh

Dmytro Shevchuk

Almer M. van der Sloot

Piotr Gaiński

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Trajectory Flow Matching with Applications to Clinical Time Series Modelling

Xi Zhang

Yuan Pu

Yuki Kawamura

Andrew Loza

Dennis Shung

Alexander Tong

Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medici… (see more)ne. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose **Trajectory Flow Matching** (TFM), which trains a Neural SDE in a *simulation-free* manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.

2024-09-25

NeurIPS.cc/2024/Conference (spotlight)

A neuronal least-action principle for real-time learning in cortical circuits

Walter Senn

Dominik Dold

Akos F. Kungl

Benjamin Ellenberger

Jakob Jordan

João Sacramento

Mihai A. Petrovici

One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal lea… (see more)st-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimizes the local somato-dendritic mismatch error within individual neurons. For output neurons, the principle implies minimizing an instantaneous behavioural error. For deep network neurons, it implies the prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory input and the motor feedback during the ongoing sensory-motor transform. Online synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic laws for global real-time computation and learning in the brain.

2024-09-23

bioRxiv (preprint)

A neuronal least-action principle for real-time learning in cortical circuits

Walter Senn

Dominik Dold

Akos F. Kungl

Benjamin Ellenberger

Jakob Jordan

João Sacramento

Mihai A. Petrovici

2024-09-23

bioRxiv (preprint)

AI content detection in the emerging information ecosystem: new obligations for media and tech companies

Alistair Knott

Dino Pedreschi

Toshiya Jitsuzumi

Susan Leavy

D. Eyers

Tapabrata Chakraborti

Andrew Trotman

Sundar Sundareswaran

Ricardo Baeza-Yates

Przemyslaw Biecek

Adrian Weller

Paul D. Teal

Subhadip Basu

Mehmet Haklidir

Virginia Morini

Stuart Russell

2024-09-21

Ethics and Information Technology (published)

A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds

Gabriele Scalia

Steven T. Rutherford

Ziqing Lu

Kerry R. Buchholz

Nicholas Skelton

Kangway Chuang

Nathaniel Diamant

Jan-Christian Hütter

Jerome-Maxim Luescher

Anh Miu

Jeff Blaney

Leo Gendelev

Elizabeth Skippington

Greg Zynda

Nia Dickson

Michał Koziarski

Aviv Regev

Man-Wah Tan

Tommasso Biancalani

The proliferation of multi-drug-resistant bacteria underscores an urgent need for novel antibiotics. Traditional discovery methods face chal… (see more)lenges due to limited chemical diversity, high costs, and difficulties in identifying structurally novel compounds. Here, we explore the integration of small molecule high-throughput screening with a deep learning-based virtual screening approach to uncover new antibacterial compounds. Leveraging a diverse library of nearly 2 million small molecules, we conducted comprehensive phenotypic screening against a sensitized Escherichia coli strain that, at a low hit rate, yielded thousands of hits. We trained a deep learning model, GNEprop, to predict antibacterial activity, ensuring robustness through out-of-distribution generalization techniques. Virtual screening of over 1.4 billion compounds identified potential candidates, of which 82 exhibited antibacterial activity, illustrating a 90X improved hit rate over the high-throughput screening experiment GNEprop was trained on. Importantly, a significant portion of these newly identified compounds exhibited high dissimilarity to known antibiotics, indicating promising avenues for further exploration in antibiotic discovery.

2024-09-14

bioRxiv (preprint)

A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds

Gabriele Scalia

Steven T. Rutherford

Ziqing Lu

Kerry R. Buchholz

Nicholas Skelton

Kangway Chuang

Nathaniel Diamant

Jan-Christian Hütter

Jerome-Maxim Luescher

Anh Miu

Jeff Blaney

Leo Gendelev

Elizabeth Skippington

Greg Zynda

Nia Dickson

Michał Koziarski

Aviv Regev

Man-Wah Tan

Tommaso Biancalani

2024-09-14

bioRxiv (preprint)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Usman Anwar

Abulhair Saparov

Javier Rando

Daniel Paleka

Miles Turpin

Peter Hase

Ekdeep Singh Lubana

Erik Jenner

Stephen Casper

Oliver Sourbut

Benjamin L. Edelman

Zhaowei Zhang

Mario Günther

Anton Korinek

Jose Hernandez-Orallo

Lewis Hammond

Eric J Bigelow

Alexander Pan

Lauro Langosco

Tomasz Korbak … (see 22 more)

Heidi Chenyu Zhang

Ruiqi Zhong

Sean O hEigeartaigh

Gabriel Recchia

Giulio Corsi

Alan Chan

Markus Anderljung

Lilian Edwards

Aleksandar Petrov

Christian Schroeder de Witt

Danqi Chen

Samuel Albanie

Sumeet Ramesh Motwani

Tegan Maharaj

Jakob Nicolaus Foerster

Philip Torr

Florian Tramèr

He He

Atoosa Kasirzadeh

Yejin Choi

David Scott Krueger

2024-09-02

TMLR (accepted)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Anirudh Goyal

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (see more). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (preprint)

arxiv.org

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Anirudh Goyal

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

2024-08-17

ArXiv (preprint)