J
Jonas Gehring
Researcher at Facebook
Publications - 24
Citations - 3795
Jonas Gehring is an academic researcher from Facebook. The author has contributed to research in topics: Artificial neural network & Bottleneck. The author has an hindex of 13, co-authored 23 publications receiving 3165 citations. Previous affiliations of Jonas Gehring include Karlsruhe Institute of Technology.
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Proceedings Article
Convolutional Sequence to Sequence Learning
TL;DR: The authors introduced an architecture based entirely on convolutional neural networks, where computations over all elements can be fully parallelized during training and optimization is easier since the number of nonlinearities is fixed and independent of the input length.
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Convolutional Sequence to Sequence Learning
TL;DR: The authors introduced an architecture based entirely on convolutional neural networks, where computations over all elements can be fully parallelized during training and optimization is easier since the number of nonlinearities is fixed and independent of the input length.
Proceedings ArticleDOI
A Convolutional Encoder Model for Neural Machine Translation
TL;DR: A faster and simpler architecture based on a succession of convolutional layers that allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies is presented.
Proceedings ArticleDOI
Extracting deep bottleneck features using stacked auto-encoders
TL;DR: It is found that increasing the number of auto-encoders in the network produces more useful features, but requires pre-training, especially when little training data is available.
Posted Content
A Convolutional Encoder Model for Neural Machine Translation
TL;DR: This article proposed a faster and simpler architecture based on a succession of convolutional layers, which allows to encode the entire source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies.