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ATAL
2008
Springer
15 years 1 months ago
Multi-robot Markov random fields
We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selectio...
Jesse Butterfield, Odest Chadwicke Jenkins, Brian ...
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 7 months 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
ECAI
2008
Springer
15 years 1 months ago
Belief revision with reinforcement learning for interactive object recognition
From a conceptual point of view, belief revision and learning are quite similar. Both methods change the belief state of an intelligent agent by processing incoming information. Ho...
Thomas Leopold, Gabriele Kern-Isberner, Gabriele P...
CVPR
2011
IEEE
14 years 8 months ago
Wavelet Belief Propagation for Large Scale Inference Problems
Ruxandra Lasowski, Art Tevs, Michael Wand, Hans-Pe...
JMLR
2010
143views more  JMLR 2010»
14 years 6 months ago
Incremental Sigmoid Belief Networks for Grammar Learning
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
James Henderson, Ivan Titov