@inproceedings{b2947492458146f9b4529efccd459e15,

title = "Convex max-product algorithms for continuous MRFs with applications to protein folding",

abstract = "This paper investigates convex belief propagation algorithms for Markov random fields (MRFs) with continuous variables. Our first contribution is a theorem generalizing properties of the discrete case to the continuous case. Our second contribution is an algorithm for computing the value of the Lagrangian relaxation of the MRF in the continuous case based on associating the continuous variables with an ever-finer interval grid. A third contribution is a particle method which uses convex max-product in re-sampling particles. This last algorithm is shown to be particularly effective for protein folding where it outperforms particle methods based on standard max-product resampling.",

author = "Jian Peng and Tamir Hazan and David McAllester and Raquel Urtasun",

year = "2011",

language = "English (US)",

isbn = "9781450306195",

series = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",

pages = "729--736",

booktitle = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",

note = "28th International Conference on Machine Learning, ICML 2011 ; Conference date: 28-06-2011 Through 02-07-2011",

}