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» Graph model selection using maximum likelihood
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TSP
2010
13 years 2 months ago
Covariance estimation in decomposable Gaussian graphical models
Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are...
Ami Wiesel, Yonina C. Eldar, Alfred O. Hero
GLVLSI
2007
IEEE
194views VLSI» more  GLVLSI 2007»
13 years 11 months ago
Probabilistic maximum error modeling for unreliable logic circuits
Reliability modeling and evaluation is expected to be one of the major issues in emerging nano-devices and beyond 22nm CMOS. Such devices would have inherent propensity for gate f...
Karthikeyan Lingasubramanian, Sanjukta Bhanja
CVPR
2004
IEEE
13 years 11 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...
ECCV
2002
Springer
14 years 9 months ago
Multimodal Data Representations with Parameterized Local Structures
Abstract. In many vision problems, the observed data lies in a nonlinear manifold in a high-dimensional space. This paper presents a generic modelling scheme to characterize the no...
Ying Zhu, Dorin Comaniciu, Stuart C. Schwartz, Vis...
TSP
2012
12 years 3 months ago
Distributed Covariance Estimation in Gaussian Graphical Models
—We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient d...
Ami Wiesel, Alfred O. Hero