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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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Generative Adversarial Networks

Ian Goodfellow, +7 more
- 10 Jun 2014 - 
TL;DR: In this article, a generative adversarial network (GAN) is proposed to estimate generative models via an adversarial process, in which two models are simultaneously trained: a generator G and a discriminator D that estimates the probability that a sample came from the training data rather than G.
Proceedings Article

On the difficulty of training recurrent neural networks

Razvan Pascanu, +2 more
TL;DR: In this article, a gradient norm clipping strategy is proposed to deal with the vanishing and exploding gradient problems in recurrent neural networks. But the proposed solution is limited to the case of RNNs.
Proceedings Article

FitNets: Hints for Thin Deep Nets

Adriana Romero, +5 more
TL;DR: This paper extends the idea of a student network that could imitate the soft output of a larger teacher network or ensemble of networks, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student.
Journal ArticleDOI

Brain tumor segmentation with Deep Neural Networks

Mohammad Havaei, +8 more
- 01 Jan 2017 - 
TL;DR: A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.
Posted Content

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Kyunghyun Cho, +8 more
- 03 Jun 2014 - 
TL;DR: Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases.