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» A Markov random field model for term dependencies
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CORR
2006
Springer
76views Education» more  CORR 2006»
14 years 9 months ago
Inconsistent parameter estimation in Markov random fields: Benefits in the computation-limited setting
Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and th...
Martin J. Wainwright
GLVLSI
2006
IEEE
193views VLSI» more  GLVLSI 2006»
15 years 3 months ago
Optimizing noise-immune nanoscale circuits using principles of Markov random fields
As CMOS devices and operating voltages are scaled down, noise and defective devices will impact the reliability of digital circuits. Probabilistic computing compatible with CMOS o...
Kundan Nepal, R. Iris Bahar, Joseph L. Mundy, Will...
ISVC
2010
Springer
14 years 8 months ago
Markov Random Field-Based Clustering for the Integration of Multi-view Range Images
Abstract. Multi-view range image integration aims at producing a single reasonable 3D point cloud. The point cloud is likely to be inconsistent with the measurements topologically ...
Ran Song, Yonghuai Liu, Ralph R. Martin, Paul L. R...
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ISCAS
2007
IEEE
106views Hardware» more  ISCAS 2007»
15 years 3 months ago
Ensemble Dependent Matrix Methodology for Probabilistic-Based Fault-tolerant Nanoscale Circuit Design
—Two probabilistic-based models, namely the Ensemble-Dependent Matrix model [1][3] and the Markov Random Field model [2], have been proposed to deal with faults in nanoscale syst...
Huifei Rao, Jie Chen, Changhong Yu, Woon Tiong Ang...
JMLR
2010
145views more  JMLR 2010»
14 years 4 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever