---
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
  bibo_doi: 10.48550/arXiv.1205.6352
  dct_date: 2012^xs_gYear
  dct_language: eng
  dct_title: Generalized sequential tree-reweighted message passing@
...