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.
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Theano: new features and speed improvements
Frédéric Bastien,Pascal Lamblin,Razvan Pascanu,James Bergstra,Ian Goodfellow,Arnaud Bergeron,Nicolas Bouchard,David Warde-Farley,Yoshua Bengio +8 more
TL;DR: New features and efficiency improvements to Theano are presented, and benchmarks demonstrating Theano's performance relative to Torch7, a recently introduced machine learning library, and to RNNLM, a C++ library targeted at recurrent neural networks.
Proceedings Article
Binarized Neural Networks
TL;DR: A binary matrix multiplication GPU kernel is written with which it is possible to run the MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy.
Proceedings Article
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
TL;DR: It is found empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold.
Proceedings Article
BinaryConnect: training deep neural networks with binary weights during propagations
TL;DR: BinaryConnect is introduced, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated, and near state-of-the-art results with BinaryConnect are obtained on the permutation-invariant MNIST, CIFAR-10 and SVHN.
Posted Content
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
TL;DR: A binary matrix multiplication GPU kernel is programmed with which it is possible to run the MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy.