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Geoffrey Hinton Computer scientist

Geoffrey Hinton investigates how neural networks can be used for learning, memory, perception and symbol processing. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications in deep learning. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, Helmholtz machines and products of experts. His current main interest is in unsupervised learning procedures for neural networks with rich sensory input.

Awards

IEEE Frank Rosenblatt Medal, 2014.

Killam Prize in Engineering, 2012.

Gerhard Herzberg Gold Medal for Science and Engineering, 2011.

IJCAI Award for Research Excellence, 2005.

David E. Rumelhart Prize, 2001.

Relevant Publications

D.E. Rumelhart et al, "Parallel distributed processing," IEEE, vol. 1, pp. 354-362, 1988.

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Appointment

Advisor Learning in Machines & Brains

Institution

Google, University of TorontoDepartment of Computer Science

Education

PhD (Artificial Intelligence) Edinburgh University

BA (Experimental Psychology) Cambridge University

Country

Canada

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