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UAI
2008
13 years 9 months ago
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
Max Welling, Yee Whye Teh, Bert Kappen
ILP
2005
Springer
14 years 1 months ago
Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops
Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never s...
Yi-Dong Shen, Qiang Yang
INFFUS
2010
143views more  INFFUS 2010»
13 years 6 months ago
A multi-agent systems approach to distributed bayesian information fusion
This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing co...
Gregor Pavlin, Patrick de Oude, Marinus Maris, Jan...
ICPR
2008
IEEE
14 years 9 months ago
Visual features with semantic combination using Bayesian network for a more effective image retrieval
In many vision problems, instead of having fully annotated training data, it is easier to obtain just a subset of data with annotations, because it is less restrictive for the use...
Sabine Barrat, Salvatore Tabbone
ML
2010
ACM
151views Machine Learning» more  ML 2010»
13 years 6 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales