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ICASSP
2010
IEEE
9 years 5 months ago
Random attributed graphs for statistical inference from content and context
Coping with Information Overload is a major challenge of the 21st century. Huge volumes and varieties of multilingual data must be processed to extract salient information. Previo...
Allen L. Gorin, Carey E. Priebe, John Grothendieck
CSDA
2010
124views more  CSDA 2010»
9 years 7 months ago
Statistical inference on attributed random graphs: Fusion of graph features and content
Abstract: Fusion of information from graph features and content can provide superior inference for an anomaly detection task, compared to the corresponding content-only or graph fe...
John Grothendieck, Carey E. Priebe, Allen L. Gorin
ECCV
2006
Springer
10 years 9 months ago
Statistical Priors for Efficient Combinatorial Optimization Via Graph Cuts
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Daniel Cremers, Leo Grady
CORR
2010
Springer
154views Education» more  CORR 2010»
9 years 7 months ago
Causal Markov condition for submodular information measures
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences among the observations that are entailed by a cau...
Bastian Steudel, Dominik Janzing, Bernhard Sch&oum...
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