Dr. Geoffrey E. Hinton

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    University of Toronto, CANADA

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    Featured Author: Geoffrey E. Hinton

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    Geoffrey E. Hinton (b. in Wimbledon, England, 6 Dec 1947) was raised in Bristol, England. He attended Cambridge University, graduating in 1970 with a B.A. honors in Experimental Psychology. He pursued further study at the University of Edinburgh, receiving his Doctorate in Artificial Intelligence in 1978. After post-doctoral work at Sussex University and the University of California, San Diego, Hinton took a faculty position at Carnegie-Mellon University. In 1987 he was made a Fellow of the Canadian Institute for Advanced Research and moved to the Computer Science department of University of Toronto. He left this post temporarily in July of 1998 to found the Gatsby Computational Neuroscience Unit at University College, London, returning to Canada in 2001. In 2006 Hinton was awarded the University of Toronto's most prestigious designation of 'University Professor'.

    In addition to his fellowship in the Canadian and British Royal Societies, Dr. Hinton is a fellow of the Cognitive Science Society and the American Association for Artificial Intelligence, and has been awarded honorary foreign membership in the American Academy of Arts and Sciences. In 2001 he was given an Honorary Doctor of Science from the University of Edinburgh, and became the first ever recipient of the "Nobel" of Cognitive Science, the David E. Rummelhart Prize.

    Throughout his career Dr. Hinton's work has focussed on the study of neural network implementations of learning, memory, perception, and symbol processing, authoring over 200 papers in these areas. Dr. Hinton was among the scientists that developed the algorithm for training neural networks through the backpropagation of error, their research culminating in the publication of the seminal two-volume work "Parallel Distributed Processing". Dr. Hinton has made further landmark contributions through his study of Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, Helmholtz machines, products of experts and deep belief nets. His current focus is on unsupervised learning of multiple layers of features for highly-structured data sets; for more information, visit http://www.cs.toronto.edu/~hinton/ .

    Scholarpedia articles:

    Boltzmann Machine. Scholarpedia, 2(5):1668 (2007)
    Deep Belief Networks. Scholarpedia, 4(5):5947 (2009)

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