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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 ChapterDOI
A view of the EM algorithm that justifies incremental, sparse, and other variants
TL;DR: In this paper, an incremental variant of the EM algorithm is proposed, in which the distribution for only one of the unobserved variables is recalculated in each E step, which is shown empirically to give faster convergence in a mixture estimation problem.
Proceedings ArticleDOI
Restricted Boltzmann machines for collaborative filtering
TL;DR: This paper shows how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies, and demonstrates that RBM's can be successfully applied to the Netflix data set.
Proceedings Article
Neighbourhood Components Analysis
TL;DR: A novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm that directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
Journal ArticleDOI
Acoustic Modeling Using Deep Belief Networks
TL;DR: It is shown that better phone recognition on the TIMIT dataset can be achieved by replacing Gaussian mixture models by deep neural networks that contain many layers of features and a very large number of parameters.
Proceedings Article
Stochastic Neighbor Embedding
Geoffrey E. Hinton,Sam T. Roweis +1 more
TL;DR: This probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images, which allows ambiguous objects, like the document count vector for the word "bank", to have versions close to the images of both "river" and "finance" without forcing the image of outdoor concepts to be located close to those of corporate concepts.