Biographical
Information
Geoffrey
Hinton received his BA in experimental psychology from
Cambridge in 1970 and his Ph.D. in Artificial Intelligence
from Edinburgh in 1978. He is currently a fellow of
the Canadian Institute for Advanced Research and professor
of Computer Science and Psychology at the University
of Toronto. He does research on ways of using neural
networks for learning, memory, perception and symbol
processing and has over 100 publications in these areas.
He was one of the researchers who introduced the back-propagation
algorithm which is now widely used for practical applications.
His other contributions to neural network research include
Boltzmann machines, distributed representations, time-delay
neural nets, mixtures of experts, and Helmholtz machines.
Abstract
The brain
learns to convert the sensory input into internal representations
of the causes of the input. It does this without having
a teacher to specify what each internal neuron ought
to be doing. Dr. Hinton describes the "wake-sleep" algorithm
which uses top-down connections to create target states
for the internal neurons. These target states can then
be used to train the bottom-up connections. The learning
rule is entirely local. The performance of the system
can be improved by using adaptive lateral connections
within each layer to ensure that the different parts
of the representation in that layer are mutually consistent.
His talk
describes joint work with Peter Dayan, Brendan Frey,
Quaid Morris and Radford Neal.
