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» Mapping for Iterative MMSE-SIC with Belief Propagation
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CORR
2007
Springer
128views Education» more  CORR 2007»
14 years 9 months ago
Equivalence of LP Relaxation and Max-Product for Weighted Matching in General Graphs
— Max-product belief propagation is a local, iterative algorithm to find the mode/MAP estimate of a probability distribution. While it has been successfully employed in a wide v...
Sujay Sanghavi
CVPR
2006
IEEE
15 years 11 months ago
Robust Tracking and Stereo Matching under Variable Illumination
Illumination inconsistencies cause serious problems for classical computer vision applications such as tracking and stereo matching. We present a new approach to model illuminatio...
Jingdan Zhang, Leonard McMillan, Jingyi Yu
TIT
2008
127views more  TIT 2008»
14 years 9 months ago
Max-Product for Maximum Weight Matching: Convergence, Correctness, and LP Duality
Abstract--Max-product "belief propagation" (BP) is an iterative, message-passing algorithm for finding the maximum a posteriori (MAP) assignment of a discrete probability...
Mohsen Bayati, Devavrat Shah, Mayank Sharma
CORR
2010
Springer
153views Education» more  CORR 2010»
14 years 9 months ago
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuf...
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny B...
CVPR
2009
IEEE
16 years 4 months ago
Alphabet SOUP: A Framework for Approximate Energy Minimization
Many problems in computer vision can be modeled using conditional Markov random fields (CRF). Since finding the maximum a posteriori (MAP) solution in such models is NP-hard, mu...
Stephen Gould (Stanford University), Fernando Amat...