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» Approximate algorithms for neural-Bayesian approaches
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JMLR
2010
129views more  JMLR 2010»
14 years 4 months ago
Expectation Truncation and the Benefits of Preselection In Training Generative Models
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
Jörg Lücke, Julian Eggert
ICASSP
2011
IEEE
14 years 1 months ago
Scalable robust hypothesis tests using graphical models
Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many app...
Divyanshu Vats, Vishal Monga, Umamahesh Srinivas, ...
CIKM
2011
Springer
13 years 10 months ago
Factorization-based lossless compression of inverted indices
Many large-scale Web applications that require ranked top-k retrieval are implemented using inverted indices. An inverted index represents a sparse term-document matrix, where non...
George Beskales, Marcus Fontoura, Maxim Gurevich, ...
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CVPR
2012
IEEE
13 years 15 days ago
Submodular dictionary learning for sparse coding
A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: ent...
Zhuolin Jiang, Guangxiao Zhang, Larry S. Davis
ICIP
2006
IEEE
15 years 11 months ago
Support Vector Machines for Camera Calibration Problem
This paper presents a statistical learning-based solution to the camera calibration problem in which the Support Vector Machines (SVM) are used for the estimation of the projectio...
Refaat M. Mohamed, Abdelrehim H. Ahmed, Ahmed Eid,...