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» Parallelizable Sampling of Markov Random Fields
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JMLR
2010
145views more  JMLR 2010»
12 years 11 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
ICCV
2011
IEEE
12 years 4 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
APPROX
2008
Springer
119views Algorithms» more  APPROX 2008»
13 years 6 months ago
The Complexity of Distinguishing Markov Random Fields
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
Andrej Bogdanov, Elchanan Mossel, Salil P. Vadhan
ICIP
1995
IEEE
14 years 5 months ago
3D super-resolution using generalized sampling expansion
A 3D super-resolution algorithm is proposed below, based on a probabilistic interpretation of the ndimensional version of Papoulis' generalized sampling theorem. The algorith...
Hassan Shekarforoush, Marc Berthod, Josiane Zerubi...
DAS
2010
Springer
13 years 6 months ago
Overlapped text segmentation using Markov random field and aggregation
Separating machine printed text and handwriting from overlapping text is a challenging problem in the document analysis field and no reliable algorithms have been developed thus f...
Xujun Peng, Srirangaraj Setlur, Venu Govindaraju, ...