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.
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Bounding the Test Log-Likelihood of Generative Models
TL;DR: A more efficient estimator is proposed, and it is proved that it provides a lower bound on the true test log-likelihood, and an unbiased estimator as the number of generated samples goes to infinity, although one that incorporates the effect of poor mixing.
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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Maxime Chevalier-Boisvert,Dzmitry Bahdanau,Salem Lahlou,Lucas Willems,Chitwan Saharia,Thien Huu Nguyen,Yoshua Bengio +6 more
TL;DR: The BabyAI research platform is introduced to support investigations towards including humans in the loop for grounded language learning and puts forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.
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Efficient EM Training of Gaussian Mixtures with Missing Data
TL;DR: This paper introduces a spanning-tree based algorithm that significantly speeds up training in these conditions of data-mining applications and observes that good results can be obtained by using the generative model to fill-in the missing values for a separate discriminant learning algorithm.
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Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
TL;DR: Variational domain-agnostic feature replay is proposed, an approach that is composed of three components: an inference module that filters the input data into domain-gnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries.
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Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Ali Farshchian,Juan Álvaro Gallego,Joseph Paul Cohen,Yoshua Bengio,Lee E. Miller,Sara A. Solla +5 more
TL;DR: In this paper, an adversarial domain adaptation network is used to match the empirical probability distribution of the residuals of the reconstructed neural signals. But, the authors do not address the domain adaptation problem in this paper.