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» On the numbers of variables to represent sparse logic functi...
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CVPR
2004
IEEE
14 years 9 months ago
Learning a Restricted Bayesian Network for Object Detection
Many classes of images have the characteristics of sparse structuring of statistical dependency and the presence of conditional independencies among various groups of variables. S...
Henry Schneiderman
SDM
2012
SIAM
322views Data Mining» more  SDM 2012»
11 years 10 months ago
Adaptive Multi-task Sparse Learning with an Application to fMRI Study
In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lass...
Xi Chen, Jingrui He, Rick Lawrence, Jaime G. Carbo...
CAV
1999
Springer
119views Hardware» more  CAV 1999»
13 years 11 months ago
A Theory of Restrictions for Logics and Automata
BDDs and their algorithms implement a decision procedure for Quanti ed Propositional Logic. BDDs are a kind of acyclic automata. Unrestricted automata (recognizing unbounded string...
Nils Klarlund
NIPS
2008
13 years 9 months ago
Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform
Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
Guangzhi Cao, Charles A. Bouman
CVPR
2011
IEEE
13 years 3 months ago
Variable Grouping for Energy Minimization
This paper addresses the problem of efficiently solving large-scale energy minimization problems encountered in computer vision. We propose an energy-aware method for merging ran...
Taesup Kim, Sebastian Nowozin, Pushmeet Kohli, Cha...