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 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

agassoussisalwane2@gmail.com

Salwane Agassoussi

Université de Montréal

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

Rim Assouel

PhD - Université de Montréal

Ayoub Atanane

Collaborating Alumni - Université du Québec à Rimouski

Stefan Bauer

Independent visiting researcher

Co-supervisor :

Guillaume Lajoie

Paul Bertin

PhD - Université de Montréal

Ghait Boukachab

Collaborating Alumni - UQAR

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

Collaborating Alumni - Université de Montréal

Eric Elmoznino

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Léna Ezzine

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

Co-supervisor :

Leo Feng

PhD - Université de Montréal

Research Intern - Université de Montréal

Ivan Grega

Research Intern - Université de Montréal

Pietro Greiner

PhD

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

Edward Hu

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

Master's Research - Université de Montréal

Co-supervisor :

Collaborating Alumni - Université de Montréal

Minsu Kim

Research Intern - Université de Montréal

Collaborating researcher - Université de Montréal

Michał Koziarski

Collaborating Alumni - Université de Montréal

Salem Lahlou

Collaborating Alumni - Université de Montréal

Seanie Lee

Collaborating Alumni - Université de Montréal

Postdoctorate - Université de Montréal

Principal supervisor :

Collaborating Alumni

Zhen Liu

Collaborating Alumni - Université de Montréal

Principal supervisor :

Liam Paull

Matt MacDermott

Collaborating Alumni - Imperial College London

PhD - Université de Montréal

Mohammed Mahfoud

Collaborating Alumni - 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 :

David Rolnick

Nassim Rahaman

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

Dounia Shaaban Kabakibo

Research Intern - 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 :

Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)

Principal supervisor :

David Rolnick

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

Zichao Yan

Collaborating Alumni - Université de Montréal

Omar G. Younis

Collaborating researcher

Collaborating researcher - KAIST

Tianyu Zhang

PhD - Université de Montréal

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Aaron Courville

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

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (see more)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.

2024-06-10

ArXiv (preprint)

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

2024-06-10

ArXiv (preprint)

VCR: Visual Caption Restoration

Tianyu Zhang

Suyuchen Wang

Lu Li

Ge Zhang

Perouz Taslakian

Sai Rajeswar

Jie Fu

Bang Liu

2024-06-10

ArXiv (preprint)

Amortizing intractable inference in diffusion models for vision, language, and control

Siddarth Venkatraman

Moksh J. Jain

Luca Scimeca

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Emmanuel Bengio

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

Nikolay Malkin

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

Amortizing intractable inference in diffusion models for vision, language, and control

Siddarth Venkatraman

Moksh J. Jain

Luca Scimeca

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Emmanuel Bengio

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

Nikolay Malkin

2024-05-31

ArXiv (preprint)

Estimating Expectations without Sampling: Neural Stein Estimation

Mohsin Hasan

Dinghuai Zhang

Cheikh Ahmed

Awa Khouna

We propose a method for estimating the expected value of a given function …

2024-05-27

approximateinference.org/AABI/2024/Symposium (accepted)

openreview.net

Attention as an RNN

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

Attention as an RNN

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

Attention as an RNN

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

The advent of Transformers marked a significant breakthrough in sequence modelling, providing a highly performant architecture capable of le… (see more)veraging GPU parallelism. However, Transformers are computationally expensive at inference time, limiting their applications, particularly in low-resource settings (e.g., mobile and embedded devices). Addressing this, we (1) begin by showing that attention can be viewed as a special Recurrent Neural Network (RNN) with the ability to compute its \textit{many-to-one} RNN output efficiently. We then (2) show that popular attention-based models such as Transformers can be viewed as RNN variants. However, unlike traditional RNNs (e.g., LSTMs), these models cannot be updated efficiently with new tokens, an important property in sequence modelling. Tackling this, we (3) introduce a new efficient method of computing attention's \textit{many-to-many} RNN output based on the parallel prefix scan algorithm. Building on the new attention formulation, we (4) introduce \textbf{Aaren}, an attention-based module that can not only (i) be trained in parallel (like Transformers) but also (ii) be updated efficiently with new tokens, requiring only constant memory for inferences (like traditional RNNs). Empirically, we show Aarens achieve comparable performance to Transformers on

2024-05-22

ArXiv (preprint)

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

Metacognitive knowledge refers to humans' intuitive knowledge of their own thinking and reasoning processes. Today's best LLMs clearly posse… (see more)ss some reasoning processes. The paper gives evidence that they also have metacognitive knowledge, including ability to name skills and procedures to apply given a task. We explore this primarily in context of math reasoning, developing a prompt-guided interaction procedure to get a powerful LLM to assign sensible skill labels to math questions, followed by having it perform semantic clustering to obtain coarser families of skill labels. These coarse skill labels look interpretable to humans. To validate that these skill labels are meaningful and relevant to the LLM's reasoning processes we perform the following experiments. (a) We ask GPT-4 to assign skill labels to training questions in math datasets GSM8K and MATH. (b) When using an LLM to solve the test questions, we present it with the full list of skill labels and ask it to identify the skill needed. Then it is presented with randomly selected exemplar solved questions associated with that skill label. This improves accuracy on GSM8k and MATH for several strong LLMs, including code-assisted models. The methodology presented is domain-agnostic, even though this article applies it to math problems.

2024-05-20

ArXiv (preprint)

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

2024-05-20

ArXiv (preprint)

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization

Miloš Nikolić

Ghouthi Boukli Hacene

Ciaran Bannon

Alberto Delmas Lascorz

Matthieu Courbariaux

Omar Mohamed Awad

Isak Edo Vivancos

Vincent Gripon

Andreas Moshovos

Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time … (see more)and energy benefits on existing hardware designs that support short bitlengths. However, the question of finding the minimum bitlength for a desired accuracy remains open. We introduce a training method for minimizing inference bitlength at any granularity while maintaining accuracy. Namely, we propose a regularizer that penalizes large bitlength representations throughout the architecture and show how it can be modified to minimize other quantifiable criteria, such as number of operations or memory footprint. We demonstrate that our method learns thrifty representations while maintaining accuracy. With ImageNet, the method produces an average per layer bitlength of 4.13, 3.76 and 4.36 bits on AlexNet, ResNet18 and MobileNet V2 respectively, remaining within 2.0%, 0.5% and 0.5% of the base TOP-1 accuracy.

2024-05-19

2024 IEEE International Symposium on Circuits and Systems (ISCAS) (published)