Y
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.
Papers
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Journal ArticleDOI
FL Games: A federated learning framework for distribution shifts
TL;DR: This work proposes FL Games, a game-theoretic framework for federated learning for learning causal features that are invariant across clients, and demonstrates that FL Games achieves high out-of-distribution performance on various benchmarks.
Journal ArticleDOI
Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D
TL;DR: This method bridges the gap between the nanoscale and mesoscale simulations of amorphous molecular systems and leverages the finite range of structural correlations for an autoregressive generation of disordered molecular aggregates up to arbitrary size from small-scale computational or experimental samples.
Journal ArticleDOI
RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software
Cheng-Hao Liu,Maksym Korablyov,Stanisław Jastrzębski,Paweł Włodarczyk-Pruszyński,Yoshua Bengio,Marwin H. S. Segler +5 more
TL;DR: A novel approach, RetroGNN, to estimate synthesizability, which can successfully filter out hard to synthesize molecules while achieving a 105 times speedup over using retrosynthesis planning software.
Posted Content
Using a Financial Training Criterion Rather than a Prediction Criterion
TL;DR: In this article, the authors show that better results can be obtained when the model is directly trained in order to maximize the financial criterion of interest, here gains and losses (including those due to transactions) incurred during trading.
Posted Content
DEUP: Direct Epistemic Uncertainty Prediction
Moksh Jain,Salem Lahlou,Hadi Nekoei,Victor Butoi,Paul Bertin,Jarrid Rector-Brooks,Maksym Korablyov,Yoshua Bengio +7 more
TL;DR: Direct Epistemic Uncertainty Prediction (DEUP) as discussed by the authors is a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty, i.e., intrinsic unpredictability.