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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.
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Regularizing Neural Networks by Penalizing Confident Output Distributions
TL;DR: In this article, the authors explore regularizing neural networks by penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning.
Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems
TL;DR: This chapter contains sections titled connectionist Representation and Tensor Product Binding: Definition and Examples, and tensor Product Representation: Properties.
Book ChapterDOI
Connectionist learning procedures
TL;DR: These relatively simple, gradient-descent learning procedures work well for small tasks, and the new challenge is to find ways of improving their convergence rate and their generalization abilities so that they can be applied to larger, more realistic tasks.
Journal ArticleDOI
Backpropagation and the brain
Timothy P. Lillicrap,Adam Santoro,Luke Marris,Colin J. Akerman,Geoffrey E. Hinton,Geoffrey E. Hinton +5 more
TL;DR: It is argued that the key principles underlying backprop may indeed have a role in brain function and induce neural activities whose differences can be used to locally approximate these signals and hence drive effective learning in deep networks in the brain.