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DMS
2010
155views Multimedia» more  DMS 2010»
13 years 8 months ago
Ontological Filters for Slow Intelligence Systems
: Slow Intelligence Systems are general-purpose systems characterized by being able to improve performance over time through a process involving enumeration, propagation, adaptatio...
Shi-Kuo Chang, Emilio Zegarra, Chia Chun Shih, Tin...
JAIR
2010
119views more  JAIR 2010»
13 years 8 months ago
Active Tuples-based Scheme for Bounding Posterior Beliefs
The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any ...
Bozhena Bidyuk, Rina Dechter, Emma Rollon
SAT
2011
Springer
263views Hardware» more  SAT 2011»
13 years 25 days ago
Failed Literal Detection for QBF
Failed literal detection (FL) in SAT is a powerful approach for preprocessing. The basic idea is to assign a variable as assumption. If boolean constraint propagation (BCP) yields ...
Florian Lonsing, Armin Biere
CORR
2010
Springer
103views Education» more  CORR 2010»
13 years 10 months ago
Random Information Spread in Networks
Let G = (V, E) be an undirected loopless graph with possible parallel edges and s, t V . Assume that s is labelled at the initial time step and that every labelled vertex copies i...
Raymond Lapus, Frank Simon, Peter Tittmann
JMLR
2010
137views more  JMLR 2010»
13 years 4 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton