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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Exponentially increasing the capacity-to-computation ratio for conditional computation in deep learning
TL;DR: A novel parametrization of weight matrices in neural networks which has the potential to increase up to exponentially the ratio of the number of parameters to computation is proposed, which is based on turning on some parameters (weight matrices) when specific bit patterns of hidden unit activations are obtained.
Proceedings ArticleDOI
Global optimization of a neural network-hidden Markov model hybrid
TL;DR: An original method for integrating artificial neural networks (ANN) with hidden Markov models (HMM) with results on speaker-independent recognition experiments using this integrated ANN-HMM system on the TIMIT continuous speech database are reported.
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ReSeg: A Recurrent Neural Network for Object Segmentation
TL;DR: The proposed network, called ReSeg, is a structured prediction architecture for images centered around deep recurrent neural networks based on the recently introduced ReNet model for object classification that can challenge the state of the art in object segmentation and may have further applications in structured prediction at large.
Convolutional neural networks for mesh-based parcellation of the cerebral cortex
Guillem Cucurull,Konrad Wagstyl,Arantxa Casanova,Petar Veličković,Estrid Jakobsen,Michal Drozdzal,Adriana Romero,Alan C. Evans,Yoshua Bengio +8 more
TL;DR: It is shown experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-ofthe-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.