---
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@
...