Geoffrey E. Hinton
homepage:http://www.cs.toronto.edu/~hinton/
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Description

Geoffrey Hinton received his BA in experimental psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is a University Professor. He is the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research.

Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. He is an honorary foreign member of the American Academy of Arts and Sciences, and a former president of the Cognitive Science Society. He received an honorary doctorate from the University of Edinburgh in 2001. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the IEEE Neural Network Pioneer award (1998) and the ITAC/NSERC award for contributions to information technology (1992).

Geoffrey Hinton investigates ways of using neural networks for learning, memory, perception and symbol processing and has over 200 publications in these areas. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, Variational learning and products of experts. His current main interest is in unsupervised learning procedures for multi-layer neural networks with rich sensory input.


Lectures:

invited talk
flag Dropout: A simple and effective way to improve neural networks
as author at  26th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe 2012,
6006 views
  invited talk
flag Deep Learning with Multiplicative Interactions
as author at  23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009,
3029 views
tutorial
flag Deep Belief Networks
as author at  Machine Learning Summer School (MLSS), Cambridge 2009,
13409 views
  lecture
flag A tutorial on Deep Learning
as author at  VideoLectures.NET - Single Lectures Series,
35266 views