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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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Proceedings Article
Modeling Human Motion Using Binary Latent Variables
TL;DR: A non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued "visible" variables that represent joint angles that makes on-line inference efficient and allows for a simple approximate learning procedure.
The EM algorithm for mixtures of factor analyzers
TL;DR: This work presents an exact Expectation{Maximization algorithm for determining the parameters of this mixture of factor analyzers which concurrently performs clustering and dimensionality reduction, and can be thought of as a reduced dimension mixture of Gaussians.
Journal ArticleDOI
Simplifying neural networks by soft weight-sharing
TL;DR: A more complicated penalty term is proposed in which the distribution of weight values is modeled as a mixture of multiple gaussians, which allows the parameters of the mixture model to adapt at the same time as the network learns.
Proceedings Article
Feudal Reinforcement Learning
Peter Dayan,Geoffrey E. Hinton +1 more
TL;DR: This paper shows how to create a Q-learning managerial hierarchy in which high level managers learning how to set tasks to their submanagers who, in turn, learn how to satisfy them.
Posted Content
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
TL;DR: This paper proposes a simpler solution that use recurrent neural networks composed of rectified linear units that is comparable to LSTM on four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem.