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
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm,Alex Fedorov,Samuel Lavoie-Marchildon,Karan Grewal,Philip Bachman,Adam Trischler,Yoshua Bengio +6 more
TL;DR: Deep InfoMax (DIM) as discussed by the authors maximizes mutual information between an input and the output of a deep neural network encoder by matching to a prior distribution adversarially.
Proceedings ArticleDOI
End-to-end attention-based large vocabulary speech recognition
TL;DR: This work investigates an alternative method for sequence modelling based on an attention mechanism that allows a Recurrent Neural Network (RNN) to learn alignments between sequences of input frames and output labels.
Book ChapterDOI
Practical recommendations for gradient-based training of deep architectures
TL;DR: In this article, the authors present a practical guide with recommendations for some of the most commonly used hyperparameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization.
Proceedings ArticleDOI
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
TL;DR: In this article, the authors extend DenseNets to semantic segmentation and achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining.
Scaling learning algorithms towards AI
TL;DR: It is argued that deep architectures have the potential to generalize in non-local ways, i.e., beyond immediate neighbors, and that this is crucial in order to make progress on the kind of complex tasks required for artificial intelligence.