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Geoffrey E. Hinton
Researcher at Google
Publications - 426
Citations - 501778
Geoffrey E. Hinton is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Generative model. The author has an hindex of 157, co-authored 414 publications receiving 409047 citations. Previous affiliations of Geoffrey E. Hinton include Canadian Institute for Advanced Research & Max Planck Society.
Papers
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Book
Bayesian learning for neural networks
TL;DR: Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural network learning methods.
Posted Content
Layer Normalization
TL;DR: In this paper, layer normalization is applied to recurrent neural networks by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case.
Journal ArticleDOI
A learning algorithm for boltzmann machines
TL;DR: A general parallel search method is described, based on statistical mechanics, and it is shown how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way.
Proceedings Article
Dynamic Routing Between Capsules
TL;DR: It is shown that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits.
Book ChapterDOI
A Practical Guide to Training Restricted Boltzmann Machines
TL;DR: This guide is an attempt to share expertise at training restricted Boltzmann machines with other machine learning researchers.