Yoshua Bengio

jamal.abouhaibeh@mila.quebec

Biography

*For media requests, please write to medias@mila.quebec.

For more information please contact Cassidy MacNeil, Senior Assistant and Operation Lead at cassidy.macneil@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

Berkes Anaïs

Collaborating researcher - Cambridge University

Principal supervisor :

berkes.anais@mila.quebec

Rim Assouel

PhD - Université de Montréal

assouelr@mila.quebec

Shahana Chatterjee

Collaborating researcher - N/A

Principal supervisor :

shahana.chatterjee@mila.quebec

sanghyeok.choi@mila.quebec

Xiaoyin Chen

PhD - Université de Montréal

xiaoyin.chen@mila.quebec

Sanghyeok Choi

Collaborating researcher - KAIST

PhD - Université de Montréal

aniket.didolkar@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Guillaume Lajoie

eric.elmoznino@mila.quebec

PhD - Université de Montréal

lena-nehale.ezzine@mila.quebec

Jean-Pierre Falet

PhD - Université de Montréal

jean-pierre.falet@mila.quebec

PhD

pietro.greiner@mila.quebec

Mohsin Hasan

PhD - Université de Montréal

mohsin.hasan@mila.quebec

thomas.jiralerspong@mila.quebec

Moksh Jain

PhD - Université de Montréal

moksh.jain@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Guillaume Lajoie

Collaborating Alumni - Université de Montréal

younesse.kaddar@mila.quebec

Minsu Kim

Collaborating researcher - Université de Montréal

minsu.kim@mila.quebec

Hyeonah Kim

Postdoctorate - Université de Montréal

Principal supervisor :

Alex Hernández-García

hyeonah.kim@mila.quebec

Jeongjae Lee

Research Intern - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

jeongjae.lee@mila.quebec

cristian-dragos.manta@mila.quebec

Song LIU

Collaborating researcher - s.o.

Collaborating researcher

Cristian Dragos Manta

PhD - Université de Montréal

Co-supervisor :

Dhanya Sridhar

Sarthak Mittal

PhD - Université de Montréal

Principal supervisor :

Independent visiting researcher - Université de Montréal

nguyenph@mila.quebec

Ali Parviz

Collaborating researcher - Ying Wu Coll of Computing

ali.parviz@mila.quebec

Lena Podina

Collaborating researcher - University of Waterloo

Principal supervisor :

elena.podina@mila.quebec

jarrid.rector-brooks@mila.quebec

Jarrid Rector-Brooks

PhD - Université de Montréal

Danyal REHMAN

Postdoctorate - Université de Montréal

danyal.rehman@mila.quebec

oliver.richardson@mila.quebec

Oli RICHARDSON

Postdoctorate - Université de Montréal

Camille Rochefort-Boulanger

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Alex Hernández-García

divya.sharma@mila.quebec

Mélisande Astrid Crystal Teng

Collaborating Alumni - Université de Montréal

Co-supervisor :

Alex Tong

Collaborating Alumni - Université de Montréal

alexander.tong@mila.quebec

Collaborating researcher - University of Cambridge

oszkar.urban@mila.quebec

Donna Vakalis

Collaborating Alumni - Université de Montréal

Co-supervisor :

donna.vakalis@mila.quebec

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

siddarth.venkatraman@mila.quebec

Collaborating researcher - Université de Montréal

esmeralda.whitammer@mila.quebec

Collaborating Alumni

omar.younis@mila.quebec

Collaborating researcher - Université de Montréal

taeyoung.yun@mila.quebec

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Tianyu Zhang

PhD - Université de Montréal

tianyu.zhang@mila.quebec

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

How Transferable Are Features in Convolutional Neural Network Acoustic Models across Languages?

Jessica A.F. Thompson

Marc Schönwiesner

Daniel Willett

Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a ta… (see more)sk and can guide the development of well-tailored learning strategies. Here we study convolutional neural network (CNN)-based acoustic models in the context of automatic speech recognition. Adapting a method proposed by [1], we measure the transferability of each layer between English, Dutch and German to assess their language-specificity. We observed three distinct regions of transferability: (1) the first two layers were entirely transferable between languages, (2) layers 2–8 were also highly transferable but we found some evidence of language specificity, (3) the subsequent fully connected layers were more language specific but could be successfully finetuned to the target language. To further probe the effect of weight freezing, we performed follow-up experiments using freeze-training [2]. Our results are consistent with the observation that CNNs converge ‘bottom up’ during training and demonstrate the benefit of freeze training, especially for transfer learning.

2021-12-31

IEEE International Conference on Acoustics, Speech and Signal Processing (unknown)

Interpolation Consistency Training for Semi-Supervised Learning

Juho Kannala

David Lopez-Paz

Arno Solin

2021-12-31

Neural Networks (published)

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

Tianyi Zhang

Shirui Zhang

Ziwei Chen

Dianbo Liu

Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communicatio… (see more)n technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays. Federated learning method exploits not only the data but the computational power of all devices in the network to achieve more efficient model training. Nevertheless, while most traditional federated learning methods work well for homogeneous data and tasks, adapting the method to heterogeneous data and task distribution is challenging. This limitation has constrained the applications of federated learning in real-world contexts, especially in healthcare settings. Inspired by the fundamental idea of meta-learning, in this study we propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue. In addition, owing to the advantage of transfer learning for model generalization, we further improve our algorithm by introducing partial parameter sharing to balance global and local learning. We name this method partial meta-federated learning (PMFL). Finally, we apply the algorithms to two medical datasets. We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.

2021-12-31

IEEE Conference on Big Data (published)

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

Eric Larsen

Andrea Lodi

2021-12-31

INFORMS Journal on Computing (published)

(Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment

Prateek Gupta

Dylan Radovic

Maarten Scholl

Andrew Williams

Christian Schroeder de Witt

Tianyu Zhang

Yang Zhang

Insufficient Social Cost of Carbon (SCC) estimation methods and short-term decision-making horizons have hindered the ability of carbon emit… (see more)ters to properly correct for the negative externalities of climate change, as well as the capacity of nations to balance economic and climate policy. To overcome these limitations, we introduce Retrospective Social Cost of Carbon Updating (ReSCCU), a novel mechanism that corrects for these limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP), a market mechanism in which polluters offload the payment of ReSCCU adjustments to insurers. To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market, which could see real-world implementation based on the engagement of a few high net-worth individuals or independent institutions.

2021-12-31

SSRN Electronic Journal (unknown)

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances,… (see more) for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.

2021-12-31

Annual Meeting of the Association for Computational Linguistics (published)

TaHiD: Tackling Data Hiding in Fake News Detection with News Propagation Networks

Adrien Benamira

Benjamin Devillers

Etienne Lesot

Ayush K. Ray

Manal Saadi

Fragkiskos D 587

Steven Bird

Ewan Klein

Edward Loper

Nat-593

Carlos Castillo

Marcelo Mendoza

Barbara Poblete

Daryna Dementieva

Alexander Panchenko

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Ashish Vaswani

Noam M. Shazeer … (see 8 more)

Niki Parmar

Adriana Romero

Pietro Lio’

Yaqing Wang

Fenglong Ma

Zhiwei Jin

Ye Yuan

Fake news with detrimental societal effects has 001 attracted extensive attention and research. De-002 spite early success, the state-of-the… (see more)-art meth-003 ods fall short of considering the propagation 004 of news. News propagates at different times 005 through different mediums, including users, 006 comments, and sources, which form the news 007 propagation network. Moreover, the serious 008 problem of data hiding arises, which means 009 that fake news publishers disguise fake news 010 as real to confuse users by deleting comments 011 that refute the rumor or deleting the news itself 012 when it has been spread widely. Existing meth-013 ods do not consider the propagation of news 014 and fail to identify what matters in the process, 015 which leads to fake news hiding in the prop-016 agation network and escaping from detection. 017 Inspired by the propagation of news, we pro-018 pose a novel fake news detection framework 019 named TaHiD, which models the propagation 020 as a heterogeneous dynamic graph and contains 021 the propagation attention module to measure 022 the influence of different propagation. Exper-023 iments demonstrate that TaHiD extracts use-024 ful information from the news propagation net-025 work and outperforms state-of-the-art methods 026 on several benchmark datasets for fake news 027 detection. Additional studies also show that 028 TaHiD is capable of identifying fake news in 029 the case of data hiding. 030

2021-12-31

(published)

www.semanticscholar.org

A Two-Stream Continual Learning System With Variational Domain-Agnostic Feature Replay

Qicheng Lao

Xiang Jiang

Mohammad Havaei

Learning in nonstationary environments is one of the biggest challenges in machine learning. Nonstationarity can be caused by either task dr… (see more)ift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This article aims to tackle this challenge with a modularized two-stream continual learning (CL) system, where the model is required to learn new tasks from a support stream and adapted to new domains in the query stream while maintaining previously learned knowledge. To deal with both drifts within and across the two streams, we propose a variational domain-agnostic feature replay-based approach that decouples the system into three modules: an inference module that filters the input data from the two streams into domain-agnostic representations, a generative module that facilitates the high-level knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We demonstrate the effectiveness of our proposed approach in addressing the two fundamental scenarios and complex scenarios in two-stream CL.

2021-12-31

IEEE Transactions on Neural Networks and Learning Systems (published)

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

David Krueger

Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (see more)ve been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.

2021-12-26

arXiv (preprint)

Long-Term Credit Assignment via Model-based Temporal Shortcuts

2021-12-12

NeurIPS.cc/2021/Workshop/DeepRL (accepted)

openreview.net

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 two datasets - Omniglot and miniImageNet 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.

2021-12-09

NeurIPS.cc/2021/Workshop/MetaLearn (poster)

openreview.net

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

Mingde Zhao

We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during plan… (see more)ning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with several sets of customized environments featuring different challenges. We consistently observe that the design allows the planning agents to generalize their learned task-solving abilities in compatible unseen environments by attending to the relevant objects, leading to better out-of-distribution generalization performance.

2021-12-06

Neural Information Processing Systems (Accept (Poster))