Geoffrey Hinton

LMB_GeoffHinton

Appointment

  • Advisor
  • Learning in Machines & Brains

Institution

  • University of Toronto
  • Google
Department of Computer Science

Country

  • Canada

Education

PhD (Artificial Intelligence), Edinburgh University
BA (Experimental Psychology), Cambridge University

About

Geoffrey Hinton is a computer scientist who 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

Rumelhart, D.E. et al. "Parallel distributed processing." IEEE 1 (1988): 354–62.

Connect

Website