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
Deep belief networks are compact universal approximators
Nicolas Le Roux,Yoshua Bengio +1 more
TL;DR: It is proved that deep but narrow feedforward neural networks with sigmoidal units can represent any Boolean expression.
Proceedings Article
Generalized Denoising Auto-Encoders as Generative Models
TL;DR: In this paper, the authors propose a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise.
Semi-supervised Learning by Entropy Minimization.
Yves Grandvalet,Yoshua Bengio +1 more
TL;DR: In this article, the authors consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data, and motivate minimum entropy regularization, which enables to incorporate unlabelled data in the standard supervised learning.
Proceedings ArticleDOI
Montreal Neural Machine Translation Systems for WMT’15
TL;DR: The Montreal Institute for Learning Algorithms (MILA) submission to WMT’15 is to evaluate this new approach to NMT on a greater variety of language pairs, using the RNNsearch architecture, which adds an attention mechanism to the encoderdecoder.
Proceedings Article
Audio Chord Recognition with Recurrent Neural Networks.
TL;DR: An efficient algorithm to search for the global mode of the output distribution while taking long-term dependencies into account is devised and the resulting method is competitive with state-of-the-art approaches on the MIREX dataset in the major/minor prediction task.