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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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On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Kyunghyun Cho, +5 more
- 03 Sep 2014 - 
TL;DR: It is shown that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase.
Journal Article

Why Does Unsupervised Pre-training Help Deep Learning?

Dumitru Erhan, +5 more
- 01 Mar 2010 - 
TL;DR: In this paper, the authors empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples, and they suggest that unsupervised pretraining guides the learning towards basins of attraction of minima that support better generalization.
Proceedings Article

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

Xavier Glorot, +3 more
TL;DR: A deep learning approach is proposed which learns to extract a meaningful representation for each review in an unsupervised fashion and clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products.
Proceedings Article

Maxout Networks

Ian Goodfellow, +4 more
TL;DR: A simple new model called maxout is defined designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique.
Proceedings Article

Semi-supervised Learning by Entropy Minimization

Yves Grandvalet, +1 more
TL;DR: This framework, which motivates minimum entropy regularization, enables to incorporate unlabeled data in the standard supervised learning, and includes other approaches to the semi-supervised problem as particular or limiting cases.