--- res: bibo_abstract: - ' This paper addresses the problem of approximate MAP-MRF inference in general graphical models. Following [36], we consider a family of linear programming relaxations of the problem where each relaxation is specified by a set of nested pairs of factors for which the marginalization constraint needs to be enforced. We develop a generalization of the TRW-S algorithm [9] for this problem, where we use a decomposition into junction chains, monotonic w.r.t. some ordering on the nodes. This generalizes the monotonic chains in [9] in a natural way. We also show how to deal with nested factors in an efficient way. Experiments show an improvement over min-sum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems. @eng' bibo_authorlist: - foaf_Person: foaf_givenName: Vladimir foaf_name: Kolmogorov, Vladimir foaf_surname: Kolmogorov foaf_workInfoHomepage: http://www.librecat.org/personId=3D50B0BA-F248-11E8-B48F-1D18A9856A87 - foaf_Person: foaf_givenName: Thomas foaf_name: Schoenemann, Thomas foaf_surname: Schoenemann dct_date: 2012^xs_gYear dct_language: eng dct_publisher: ArXiv@ dct_title: Generalized sequential tree-reweighted message passing@ ...