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
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PhD - Université de Montréal
Collaborating researcher - KAIST
PhD - Université de Montréal
Independent visiting researcher
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PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
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PhD - Université de Montréal
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Collaborating Alumni - Université de Montréal
Postdoctorate - Université de Montréal
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Postdoctorate - Université de Montréal
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Independent visiting researcher - Université de Montréal
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Collaborating researcher - Ying Wu Coll of Computing
Collaborating researcher - University of Waterloo
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PhD - Université de Montréal
Postdoctorate - Université de Montréal
Postdoctorate - Université de Montréal
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Publications

blocks: Version 0.2.0
A Theano framework for building and training neural networks
Generative Adversarial Nets
Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the acad… (see more)emia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.
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
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.