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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.
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