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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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Disentangling factors of variation for facial expression recognition

TL;DR: A semi-supervised approach to solve the task of emotion recognition in 2D face images using recent ideas in deep learning for handling the factors of variation present in data, beating the state-of-the-art on a recently proposed dataset for facial expression recognition.
Posted Content

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

TL;DR: The 30M Factoid Question-Answer Corpus is presented, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions.
Proceedings Article

Equilibrated adaptive learning rates for non-convex optimization

TL;DR: In this article, the authors show that the Jacobi preconditioner has undesirable behavior in the presence of both positive and negative curvature, and present theoretical and empirical evidence that the so-called equilibration pre-conditioner is comparatively better suited to non-convex problems.
Proceedings Article

Topmoumoute Online Natural Gradient Algorithm

TL;DR: An efficient, general, online approximation to the natural gradient descent which is suited to large scale problems and much faster convergence in computation time and in number of iterations with TONGA than with stochastic gradient descent, even on very large datasets.
Posted Content

Pylearn2: a machine learning research library

TL;DR: A brief history of the library, an overview of its basic philosophy, a summary of the Library's architecture, and a description of how the Pylearn2 community functions socially are given.