---
res:
bibo_abstract:
- "We consider the problem of inference in a graphical model with binary variables.
While in theory it is arguably preferable to compute marginal probabilities, in
practice researchers often use MAP inference due to the availability of efficient
discrete optimization algorithms. We bridge the gap between the two approaches
by introducing the Discrete Marginals technique in which approximate marginals
are obtained by minimizing an objective function with unary and pairwise terms
over a discretized domain. This allows the use of techniques originally developed
for MAP-MRF inference and learning. We explore two ways to set up the objective
function - by discretizing the Bethe free energy and by learning it from training
data. Experimental results show that for certain types of graphs a learned function
can outperform the Bethe approximation. We also establish a link between the Bethe
free energy and submodular functions.\r\n@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Filip
foaf_name: Korc, Filip
foaf_surname: Korc
foaf_workInfoHomepage: http://www.librecat.org/personId=476A2FD6-F248-11E8-B48F-1D18A9856A87
- 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: Christoph
foaf_name: Lampert, Christoph
foaf_surname: Lampert
foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0001-8622-7887
dct_date: 2012^xs_gYear
dct_language: eng
dct_publisher: ICML@
dct_title: Approximating marginals using discrete energy minimization@
...