Manifold Stochastic Dynamics for Bayesian Learning
Abstract
We propose a new Markov Chain Monte Carlo algorithm which is a gen(cid:173) eralization of the stochastic dynamics method. The algorithm performs exploration of the state space using its intrinsic geometric structure, facil(cid:173) itating efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to perform at least as well as the best state-of-the-art method while consuming considerably less time.
Cite
Text
Zlochin and Baram. "Manifold Stochastic Dynamics for Bayesian Learning." Neural Information Processing Systems, 1999.Markdown
[Zlochin and Baram. "Manifold Stochastic Dynamics for Bayesian Learning." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/zlochin1999neurips-manifold/)BibTeX
@inproceedings{zlochin1999neurips-manifold,
title = {{Manifold Stochastic Dynamics for Bayesian Learning}},
author = {Zlochin, Mark and Baram, Yoram},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {694-700},
url = {https://mlanthology.org/neurips/1999/zlochin1999neurips-manifold/}
}