eas@utcsrgv.UUCP (Ann Struthers) (11/19/84)
Subject: Computation in Networks of Stochastic Computing Elements Newsgroups: ont.events Distribution: ont *** REPLACE THIS LINE WITH YOUR MESSAGE *** COMPUTATION IN NETWORKS OF STOCHASTIC COMPUTING ELEMENTS Tuesday, November 20, 1984 11:00 A.M Sandford Fleming Building 1105 Professor Geoffrey Hinton Computer Science Department Carnegie Mellon University Computer vision systems need to be able to perform best-fit searches rapidly. Given a large set of candidate hypotheses about how to interpret parts or aspects of an image, and a set of plausible constraints between pairs of local hypotheses, truth-values must be assigned to the hypotheses so as too minimize the total violation of the plausible constraints. This is a combinatorial optimization problem. It can be solved by allowing a network of simple computing elements to settle into a stable state. Each element represents a hypothesis, and the interactions between the elements implement the constraints. Until recently it has been very difficult to analyze this kind of cooperative computation, because the elements must be non-linear and cross-compiled. If, however, the elements adopt discrete states according to a particular stochastic function of their inputs, it is possible to use statistical mechanics to describe the behaviour of the whole system. An unexpected result of this analysis is that there is a very simple relationship between the probability of settling into a particular global state and the strengths of the local couplings between the individual computing elements. This relationship allows a cooperative system to learn the implicit constraints in any domain simply by being told whether the state it settles into is correct. The learning algorithm converges on a set of weights which is optimal, i.e. a set of weights which minimizes the difference between the probabilities with which the system settles on particular interpretations and the probabilities that these interpretations are correct.