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139
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JMLR
2010
137views more  JMLR 2010»
14 years 10 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
JMLR
2010
202views more  JMLR 2010»
14 years 10 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
136
Voted
JMLR
2010
145views more  JMLR 2010»
14 years 10 months ago
Event based text mining for integrated network construction
The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining technique...
Yvan Saeys, Sofie Van Landeghem, Yves Van de Peer
136
Voted
ML
2010
ACM
175views Machine Learning» more  ML 2010»
14 years 10 months ago
Concept learning in description logics using refinement operators
With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applica...
Jens Lehmann, Pascal Hitzler
138
Voted
TIP
2010
141views more  TIP 2010»
14 years 10 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina