Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning

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

Collaborating Alumni - McGill University
Collaborating researcher - Cambridge University
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PhD - Université de Montréal
Collaborating researcher - N/A
Principal supervisor :
PhD - Université de Montréal
PhD - Université de Montréal
Collaborating Alumni - Université de Montréal
Collaborating researcher - Université de Montréal
Postdoctorate - Université de Montréal
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Research Intern - KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
Collaborating researcher - s.o.
PhD - Université de Montréal
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PhD - Université de Montréal
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Independent visiting researcher - Université de Montréal
Collaborating researcher - Ying Wu Coll of Computing
Collaborating researcher - University of Waterloo
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Postdoctorate - Université de Montréal
Postdoctorate - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
Collaborating Alumni - Université de Montréal
Co-supervisor :
Collaborating Alumni - Université de Montréal
Collaborating researcher - University of Cambridge
Collaborating Alumni - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher - Université de Montréal
Collaborating researcher - Université de Montréal
PhD - McGill University
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Publications

ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Adriana Romero
Matteo Matteucci
Marco Ciccone
We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and th… (see more)e capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on https://github.com/fvisin/reseg.
Statistical Language and Speech Processing
Fethi Bougares
Horia Cucu
Corneliu Burileanu
Myung-Jae Kim
Il-ho Yang
Jordan Rodu
Dean Phillips Foster
Weichen Wu
Stefan Bott
Felix Stahlberg
Luis A. Trindade
Hao Wang
Theano: Deep Learning on GPUs with Python
In this paper, we present Theano 1 , a framework in the Python programming language for defining, optimizing and evaluating expressions invo… (see more)lving high-level operations on tensors. Theano offers most of NumPy’s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of facilitating research in deep learning. The Deep Learning Tutorials 2 introduce recent advances in deep learning, and showcase how Theano
COVI White Paper-Version 1.1
H. Alsdurf
T. Deleu
Prateek Gupta
Daphne Ippolito
R. Janda
Max Jarvie
Tyler Kolody
S. Krastev
Robert Obryk
D. Pilat
Nasim Rahaman
I. Rish
J. Rousseau
Abhinav Sharma
B. Struck … (see 3 more)
Yun William Yu
Theano: A CPU and GPU Math Compiler in Python
Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized nati… (see more)ve machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.