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.

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Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

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PhD - McGill University

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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

The Effect of diversity in Meta-Learning

Ramnath Kumar

Tristan Deleu

Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that task … (see more)distribution plays a vital role in the performance of the model. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; we study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms. For this experiment, we train on multiple datasets, and with three broad classes of meta-learning models - Metric-based (i.e., Protonet, Matching Networks), Optimization-based (i.e., MAML, Reptile, and MetaOptNet), and Bayesian meta-learning models (i.e., CNAPs). Our experiments demonstrate that the effect of task diversity on all these algorithms follows a similar trend, and task diversity does not seem to offer any benefits to the learning of the model. Furthermore, we also demonstrate that even a handful of tasks, repeated over multiple batches, would be sufficient to achieve a performance similar to uniform sampling and draws into question the need for additional tasks to create better models.

2023-06-26

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Constant Memory Attention Block

Leo Feng

Frederick Tung

Hossein Hajimirsadeghi

Mohamed Osama Ahmed

2023-06-20

ICML.cc/2023/Workshop/ES-FoMO (poster)

BatchGFN: Generative Flow Networks for Batch Active Learning

Shreshth A Malik

Salem Lahlou

Andrew Jesson

Moksh J. Jain

Nikolay Malkin

Tristan Deleu

Yarin Gal

We introduce BatchGFN—a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points pro… (see more)portional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model parameters, BatchGFN is able to construct highly informative batches for active learning in a principled way. We show our approach enables sampling near-optimal utility batches at inference time with a single forward pass per point in the batch in toy regression problems. This alleviates the computational complexity of batch-aware algorithms and removes the need for greedy approximations to find maximizers for the batch reward. We also present early results for amortizing training across acquisition steps, which will enable scaling to real-world tasks.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

Chris Emezue

Alexandre Drouin

Tristan Deleu

Stefan Bauer

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

GFlowNets for Causal Discovery: an Overview

Dragos Cristian Manta

Edward J Hu

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Simulation-Free Schrödinger Bridges via Score and Flow Matching

Alexander Tong

Nikolay Malkin

Kilian FATRAS

Lazar Atanackovic

Yanlei Zhang

Guillaume Huguet

Guy Wolf

We present simulation-free score and flow matching ([SF]…

2023-06-19

ICML.cc/2023/Workshop/Frontiers4LCD (published)

Thompson Sampling for Improved Exploration in GFlowNets

Jarrid Rector-Brooks

Kanika Madan

Moksh J. Jain

Maksym Korablyov

Cheng-Hao Liu

Sarath Chandar

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over composition… (see more)al objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

Ayush K Chakravarthy

Trang M. Nguyen

Anirudh Goyal

Michael Curtis Mozer

2023-05-31

ArXiv (preprint)

arxiv.org

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

Dinghuai Zhang

Hanjun Dai

Nikolay Malkin

Aaron Courville

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)

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

Paul F. Christiano … (see 1 more)

Allan Dafoe

2023-05-24

ArXiv (preprint)

arxiv.org

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

Colleen J Gillon

Jérôme A. Lecoq

Jason E. Pina

Ruweida Ahmed

Yazan N. Billeh

Shiella Caldejon

Peter Groblewski

Timothy M. Henley

India Kato

Eric Lee

Jennifer Luviano

Kyla Mace

Chelsea Nayan

Thuyanh V. Nguyen

Kat North

Jed Perkins

Sam Seid

Matthew T. Valley

Ali Williford

Yoshua Bengio … (see 3 more)

Timothy P. Lillicrap

Joel Zylberberg

Blake Richards

2023-05-17

Scientific Data (published)

Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model

Mahsa Taghiakbari

Sina Hamidi Ghalehjegh

Emmanuel Jehanno

Tess Berthier

Lisa Di Jorio

Saber Ghadakzadeh

Alan Barkun

Mark Takla

Mickael Bouin

Eric Deslandres

Simon Bouchard

Sacha Sidani

Daniel von Renteln

Abstract Background and aims Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm comple… (see more)teness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps. Methods We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks. Results After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%). Conclusion This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality.

2023-05-02

Journal of the Canadian Association of Gastroenterology (published)