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
More filters
Journal Article
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TL;DR: Denoising autoencoders as mentioned in this paper are trained locally to denoise corrupted versions of their inputs, which is a straightforward variation on the stacking of ordinary autoencoder.
Proceedings ArticleDOI
On the Properties of Neural Machine Translation: Encoder--Decoder Approaches
Kyunghyun Cho,Bart van Merriënboer,Dzmitry Bahdanau,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +5 more
TL;DR: In this paper, a gated recursive convolutional neural network (GRNN) was proposed to learn a grammatical structure of a sentence automatically, which performed 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.
Posted Content
How transferable are features in deep neural networks
TL;DR: This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.
Proceedings ArticleDOI
Curriculum learning
TL;DR: It is hypothesized that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions).