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TIP
2008
133views more  TIP 2008»
13 years 10 months ago
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
Jason K. Johnson, Alan S. Willsky
TSE
1998
129views more  TSE 1998»
13 years 9 months ago
Inferring Declarative Requirements Specifications from Operational Scenarios
—Scenarios are increasingly recognized as an effective means for eliciting, validating, and documenting software requirements. This paper concentrates on the use of scenarios for...
Axel van Lamsweerde, Laurent Willemet
BIOINFORMATICS
2006
124views more  BIOINFORMATICS 2006»
13 years 10 months ago
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
Motivation Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular...
Guido Sanguinetti, Neil D. Lawrence, Magnus Rattra...
IROS
2008
IEEE
144views Robotics» more  IROS 2008»
14 years 4 months ago
Learning nonparametric policies by imitation
— A long cherished goal in artificial intelligence has been the ability to endow a robot with the capacity to learn and generalize skills from watching a human teacher. Such an ...
David B. Grimes, Rajesh P. N. Rao
NIPS
2008
13 years 11 months ago
Hebbian Learning of Bayes Optimal Decisions
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models fo...
Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass