Cooled and Relaxed Survey Propagation for MRFs
Abstract
We describe a new algorithm, Relaxed Survey Propagation (RSP), for finding MAP configurations in Markov random fields. We compare its performance with state-of-the-art algorithms including the max-product belief propagation, its se- quential tree-reweighted variant, residual (sum-product) belief propagation, and tree-structured expectation propagation. We show that it outperforms all ap- proaches for Ising models with mixed couplings, as well as on a web person disambiguation task formulated as a supervised clustering problem.
Cite
Text
Chieu et al. "Cooled and Relaxed Survey Propagation for MRFs." Neural Information Processing Systems, 2007.Markdown
[Chieu et al. "Cooled and Relaxed Survey Propagation for MRFs." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/chieu2007neurips-cooled/)BibTeX
@inproceedings{chieu2007neurips-cooled,
title = {{Cooled and Relaxed Survey Propagation for MRFs}},
author = {Chieu, Hai L. and Lee, Wee S. and Teh, Yee W.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {297-304},
url = {https://mlanthology.org/neurips/2007/chieu2007neurips-cooled/}
}