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» Optimal Monte Carlo Algorithms
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CVPR
2000
IEEE
16 years 6 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
CVPR
2001
IEEE
16 years 6 months ago
A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition
The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognit...
J. Ross Beveridge, Kai She, Bruce A. Draper, Geof ...
DAC
2008
ACM
16 years 5 months ago
Stochastic integral equation solver for efficient variation-aware interconnect extraction
In this paper we present an efficient algorithm for extracting the complete statistical distribution of the input impedance of interconnect structures in the presence of a large n...
Tarek Moselhy, Luca Daniel
IROS
2009
IEEE
156views Robotics» more  IROS 2009»
15 years 10 months ago
Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements
— We consider the problem of tracking multiple moving robots using noisy sensing of inter-robot and interbeacon distances. Sensing is local: there are three fixed beacons at kno...
Jeremy Schiff, Erik B. Sudderth, Kenneth Y. Goldbe...
ACCV
2009
Springer
15 years 10 months ago
A Smarter Particle Filter
Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is base...
Xiaoqin Zhang, Weiming Hu, Steve J. Maybank