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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Proceedings Article
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach
Grégoire Mesnil,Yann N. Dauphin,Xavier Glorot,Salah Rifai,Yoshua Bengio,Ian Goodfellow,Erick Lavoie,Xavier Muller,Guillaume Desjardins,David Warde-Farley,Pascal Vincent,Aaron Courville,James Bergstra +12 more
TL;DR: In this article, the authors describe different kinds of layers they trained for learning representations in the setting of the Unsupervised and Transfer Learning Challenge, and the strategy of their team won the final phase of the challenge.
Proceedings ArticleDOI
The Pytorch-kaldi Speech Recognition Toolkit
TL;DR: The PyTorch-Kaldi project as discussed by the authors aims to bridge the gap between these popular toolkits, trying to inherit the efficiency of Kaldi and the flexibility of Pytorch.
Posted Content
Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
TL;DR: The proposed multi-way, multilingual neural machine translation approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only linearly with the number of languages.
Proceedings Article
Improving Generative Adversarial Networks with Denoising Feature Matching
David Warde-Farley,Yoshua Bengio +1 more
TL;DR: An augmented training procedure for generative adversarial networks designed to address shortcomings of the original by directing the generator towards probable configurations of abstract discriminator features is proposed.
Proceedings Article
Convex Neural Networks
TL;DR: Training multi-layer neural networks in which the number of hidden units is learned can be viewed as a convex optimization problem, which involves an infinite number of variables but can be solved by incrementally inserting a hidden unit at a time.