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Prof Geoffrey Hinton
Canadian Institute for Advanced Research &
University of Toronto
What Kind of a Graphical Model is the Brain?
If neurons are treated as latent variables, our visual systems are non-linear, densely-connected graphical models containing billions of variables and thousands of billions of parameters. Current algorithms would have difficulty learning a graphical model of this scale. Starting with an algorithm that has difficulty learning more than a few thousand parameters, I describe a series of progressively better learning algorithms all of which are designed to run on neuron-like hardware. The latest member of this series can learn deep, multi-layer belief nets quite rapidly. It turns a generic network with three hidden layers and 1.7 million connections into a very good generative model of hand-written digits. After learning, the model gives classification performance that is comparable to the best discriminative methods.
For Prof Hinton's biography, see the IJCAI Awards page
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