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JMLR
2010
160views more  JMLR 2010»
15 years 1 months ago
Neural conditional random fields
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Mar...
Trinh Minh Tri Do, Thierry Artières
PE
2010
Springer
170views Optimization» more  PE 2010»
15 years 5 months ago
Approximating passage time distributions in queueing models by Bayesian expansion
We introduce Bayesian Expansion (BE), an approximate numerical technique for passage time distribution analysis in queueing networks. BE uses a class of Bayesian networks to appro...
Giuliano Casale
AAAI
1990
15 years 8 months ago
Constructor: A System for the Induction of Probabilistic Models
The probabilistic network technology is a knowledgebased technique which focuses on reasoning under uncertainty. Because of its well defined semantics and solid theoretical founda...
Robert M. Fung, Stuart L. Crawford
JAIR
2011
129views more  JAIR 2011»
15 years 1 months ago
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an ...
Wei Li 0002, Pascal Poupart, Peter van Beek
ICOIN
2007
Springer
16 years 1 months ago
Analyzing and Modeling Router-Level Internet Topology
Measurement studies on the Internet topology show that connectivities of nodes exhibit power–law attribute, but it is apparent that only the degree distribution does not determin...
Ryota Fukumoto, Shin'ichi Arakawa, Tetsuya Takine,...