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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Drawing and Recognizing Chinese Characters with Recurrent Neural Network
TL;DR: Wang et al. as mentioned in this paper proposed a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generator model for drawing (generating) Chinese characters.
Proceedings Article
A Neural Probabilistic Language Model
TL;DR: This work proposes to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences.
Theano: Deep Learning on GPUs with Python
James Bergstra,Frédéric Bastien,Olivier Breuleux,Pascal Lamblin,Razvan Pascanu,Olivier Delalleau,Guillaume Desjardins,David Warde-Farley,Ian Goodfellow,Arnaud Bergeron,Yoshua Bengio +10 more
TL;DR: This paper presents Theano, a framework in the Python programming language for defining, optimizing and evaluating expressions involving high-level operations on tensors, and adds automatic symbolic differentiation, GPU support, and faster expression evaluation.
Posted Content
Better Mixing via Deep Representations
TL;DR: It is proposed that the higher-level samples fill more uniformly the space they occupy and the high-density manifolds tend to unfold when represented at higher levels, and mixing between modes would be more efficient at higher Levels of representation.
Label Propagation and Quadratic Criterion.
TL;DR: In this article, the authors show how these different algorithms can be cast into a common framework where one minimizes a quadratic cost criterion whose closed-form solution is found by solving a linear system of size n (total number of data points).