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Yoshua Bengio

Researcher at Université de Montréal

Publications -  1146
Citations -  534376

Yoshua Bengio is an academic researcher from Université de Montréal. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 202, co-authored 1033 publications receiving 420313 citations. Previous affiliations of Yoshua Bengio include McGill University & Centre de Recherches Mathématiques.

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PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

TL;DR: PhAST as mentioned in this paper proposes task-specific improvements applicable to most architectures, enhancing both computational efficiency and accuracy for the Open Catalyst Project OC20 dataset, including improvements in graph creation step, atom representation, energy prediction head, and force prediction head.
Proceedings ArticleDOI

IAPR keynote lecture IV: Deep learning

TL;DR: This talk will summarize the advances that have made these breakthroughs possible, and end with questions about some major challenges still ahead of researchers in order to continue the climb towards AI-level competence.
Journal ArticleDOI

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions

TL;DR: In this paper , a joint energy-based generative flow network (JEBGFN) is proposed to solve the problem of incompatibility between the inductive optimization biases in training the generator and the generator.
Posted Content

Comment améliorer la capacité de généralisation des algorithmes d'apprentissage pour la prise de décisions financières

TL;DR: In this paper, the authors present and propose several methods to improve the capacity of generalization of the learning algorithms in a context of financial decision-making, which aim at controlling the capacity to limit the problem of the over-training.
Posted Content

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

TL;DR: Korbit as discussed by the authors is a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system that uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online.