Weight-space probability densities and convergence times for stochastic learning

The authors extend the theory of search dynamics for stochastic learning algorithms, address the time evolution of the weight-space probability density and the distribution of convergence times, with particular attention given to escape from local optima, and develop a theoretical framework that describes the evolution of the weight-space probability density. The primary results are exact predictions of the statistical distribution of convergence times for simple backpropagation and competitive learning problems