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» Parameterized Complexity and Approximation Algorithms
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162
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CORR
2011
Springer
174views Education» more  CORR 2011»
14 years 6 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato
137
Voted
CMSB
2008
Springer
15 years 4 months ago
Automatic Complexity Analysis and Model Reduction of Nonlinear Biochemical Systems
Kinetic models for biochemical systems often comprise a large amount of coupled differential equations with species concentrations varying on different time scales. In this paper w...
Dirk Lebiedz, Dominik Skanda, Marc Fein
ECRTS
2004
IEEE
15 years 6 months ago
An Event Stream Driven Approximation for the Analysis of Real-Time Systems
This paper presents a new approach to understand the event stream model. Additionally a new approximation algorithm for the feasibility test of the sporadic and the generalized mu...
Karsten Albers, Frank Slomka
164
Voted
ICASSP
2011
IEEE
14 years 6 months ago
Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals
In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly spa...
Shubha Shedthikere, Ananthanarayanan Chockalingam
JMM2
2007
96views more  JMM2 2007»
15 years 2 months ago
A Framework for Linear Transform Approximation Using Orthogonal Basis Projection
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
Yinpeng Chen, Hari Sundaram