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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 19 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
EMMCVPR
2001
Springer
13 years 9 months ago
A Double-Loop Algorithm to Minimize the Bethe Free Energy
Recent work (Yedidia, Freeman, Weiss [22]) has shown that stable points of belief propagation (BP) algorithms [12] for graphs with loops correspond to extrema of the Bethe free ene...
Alan L. Yuille
UAI
2003
13 years 6 months ago
Approximate Inference and Constrained Optimization
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspo...
Tom Heskes, Kees Albers, Bert Kappen
NIPS
2000
13 years 6 months ago
Generalized Belief Propagation
In an important recent paper, Yedidia, Freeman, and Weiss [11] showed that there is a close connection between the belief propagation algorithm for probabilistic inference and the...
Jonathan S. Yedidia, William T. Freeman, Yair Weis...
ECCV
2006
Springer
14 years 6 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields
One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with som...
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...