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

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

Joyce Chai

Independent visiting researcher

Principal supervisor :

Siva Reddy

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

David Rolnick

Xiaoyin Chen

PhD - Université de Montréal

Sanghyeok Choi

Collaborating researcher - KAIST

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

Collaborating Alumni - Université de Montréal

Desmond Elliott

Independent visiting researcher

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Jean-Pierre Falet

PhD - Université de Montréal

PhD - Université de Montréal

PhD

PhD - Université de Montréal

Edward Hu

PhD - Université de Montréal

Moksh Jain

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Collaborating Alumni - Université de Montréal

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 :

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

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

Collaborating researcher - University of Waterloo

Principal supervisor :

David Rolnick

Nassim Rahaman

Collaborating Alumni - Max-Planck-Institute for Intelligent Systems

Amine RAZIG

Collaborating researcher - Université de Montréal

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

Abhik Roychoudhury Roychoudhury

Independent visiting researcher

Principal supervisor :

Siva Reddy

Luca Scimeca

Postdoctorate - Université de Montréal

Collaborating Alumni - Université de Montréal

Marcin Sendera

Collaborating Alumni - Université de Montréal

Divya Sharma

Postdoctorate

Co-supervisor :

Alex Hernandez-Garcia

Marco Stock

Independent visiting researcher - Technical University of Munich

Mélisande Astrid Crystal Teng

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

Ivan Titov

Independent visiting researcher

Principal supervisor :

Siva Reddy

Alex Tong

Collaborating Alumni - Université de Montréal

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher

Collaborating researcher - Université de Montréal

Nicole Zhang

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Aaron Courville

Tianyu Zhang

PhD - Université de Montréal

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Harry Zhao

Collaborating Alumni - 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

Learning diverse attacks on large language models for robust red-teaming and safety tuning

Juho Lee

Sung Ju Hwang

Moksh J. Jain

Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.

2024-05-28

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

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

Attention as an RNN

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

Attention as an RNN

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)

Attention as an RNN

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

Attention as an RNN

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

2024-05-22

ArXiv (preprint)

Attention as an RNN

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

Greg Mori

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)