title: Manifold stochastic dynamics for Bayesian learning.
abstract: We propose a new Markov Chain Monte Carlo algorithm, which is a generalization of the stochastic dynamics method. The algorithm performs exploration of the state-space using its intrinsic geometric structure, which facilitates efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to produce results comparable to the best state-of-the-art method while consuming considerably less time.
authors.affiliation: Department of Computer Science, Technion--Israel Institute of Technology, Technion City, Haifa 32000, Israel.
authors.forename: M
authors.initials: M
authors.lastname: Zlochin
authors.forename: Y
authors.initials: Y
authors.lastname: Baram