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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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Discovering binary codes for documents by learning deep generative models.
TL;DR: A deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document is described, which allows more accurate and much faster retrieval than latent semantic analysis.
Proceedings Article
Recognizing Handwritten Digits Using Mixtures of Linear Models
TL;DR: An EM-based algorithm in which the M-step is computationally straightforward principal components analysis (PCA), and incorporating tangent-plane information about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance.
Journal ArticleDOI
Separating figure from ground with a parallel network.
TL;DR: The network model is too simplified to serve as a model of human performance, but it does demonstrate that one global property of outlines can be computed through local interactions in a parallel network.
Proceedings Article
Conditional restricted Boltzmann machines for structured output prediction
TL;DR: In this article, the authors argue that Contrastive Divergence-based learning may not be suitable for training conditional restricted Boltzmann machines (CRBMs) for structured output prediction.
Proceedings Article
Evaluation of Adaptive Mixtures of Competing Experts
TL;DR: Simulations reveal that the modular architecture, composed of competing expert networks, suggested by Jacobs, Jordan, Nowlan and Hinton (1991), is capable of uncovering interesting decompositions in a complex task.