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282
Efficient belief propagation for early vision
 In CVPR
, 2004
"... Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical u ..."
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Cited by 515 (8 self)
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the standard algorithm by several orders of magnitude. In practice we obtain stereo, optical flow and image restoration algorithms that are as accurate as other global methods (e.g., using the Middlebury stereo benchmark) while being as fast as local techniques. 1
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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edgements We thank Tommi Jaakkola, David Heckerman and David MacKay for useful discussions. We also thank Randy Miller and the University of Pittsburgh for the use of the QMRDT database. Supported by MURI ARO DAAH049610341. algorithm. These approaches are guaranteed to find local maxima, but do
Fixing MaxProduct: Convergent Message Passing Algorithms for MAP LPRelaxations
"... We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to maxproduct but unlike maxproduct it always converges, and can be proven to find the exact MAP solution in various settings. The algorithm is derived via bloc ..."
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Cited by 160 (14 self)
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We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to maxproduct but unlike maxproduct it always converges, and can be proven to find the exact MAP solution in various settings. The algorithm is derived via
Compiler algorithms for optimizing locality and . . .
, 2000
"... Distributedmemory messagepassing machines deliver scalable performance but are difficult to program. Sharedmemory machines, on the other hand, are easier to program but obtaining scalable performance with large number of processors is difficult. Recently, scalable machines based on logically sh ..."
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Cited by 11 (3 self)
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Distributedmemory messagepassing machines deliver scalable performance but are difficult to program. Sharedmemory machines, on the other hand, are easier to program but obtaining scalable performance with large number of processors is difficult. Recently, scalable machines based on logi
COMPILER ALGORITHMS
, 1998
"... Distributedmemory messagepassing machines deliver scalable performance but are difficult to program. Sharedmemory machines, on the other hand, are easier to program but obtaining scalable performance with large number of processors is difficult. Recently, scalable machines based on logically shar ..."
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Distributedmemory messagepassing machines deliver scalable performance but are difficult to program. Sharedmemory machines, on the other hand, are easier to program but obtaining scalable performance with large number of processors is difficult. Recently, scalable machines based on logically
Continuouslyadaptive discretization for messagepassing algorithms
"... ContinuouslyAdaptive Discretization for MessagePassing (CADMP) is a new messagepassing algorithm for approximate inference. Most messagepassing algorithms approximate continuous probability distributions using either: a family of continuous distributions such as the exponential family; a partic ..."
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Cited by 1 (0 self)
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ContinuouslyAdaptive Discretization for MessagePassing (CADMP) is a new messagepassing algorithm for approximate inference. Most messagepassing algorithms approximate continuous probability distributions using either: a family of continuous distributions such as the exponential family; a
A primaldual messagepassing algorithm for approximated large scale structured prediction
 In Advances in Neural Information Processing Systems 23
, 2010
"... In this paper we propose an approximated structured prediction framework for large scale graphical models and derive messagepassing algorithms for learning their parameters efficiently. We first relate CRFs and structured SVMs and show that in CRFs a variant of the logpartition function, known as ..."
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Cited by 38 (19 self)
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as the softmax, smoothly approximates the hinge loss function of structured SVMs. We then propose an intuitive approximation for the structured prediction problem, using duality, based on a local entropy approximation and derive an efficient messagepassing algorithm that is guaranteed to converge. Unlike
Convergent messagepassing algorithms for inference over general graphs with convex free energy
 In The 24th Conference on Uncertainty in Artificial Intelligence (UAI
, 2008
"... Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpoints of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails to conv ..."
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Cited by 25 (8 self)
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Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpoints of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails
A New Approach to Clustering Biological Data Using Message Passing
 In Proceedings of 2004 IEEE Computer Society Bioinformatics Conference (CSB04
"... Clustering algorithms are widely used in bioinformatics to classify data, as in the analysis of gene expression and in the building of phylogenetic trees. Biological data often describe parallel and spontaneous processes. To capture these features, we propose a new clustering algorithm that employs ..."
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Cited by 5 (4 self)
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Clustering algorithms are widely used in bioinformatics to classify data, as in the analysis of gene expression and in the building of phylogenetic trees. Biological data often describe parallel and spontaneous processes. To capture these features, we propose a new clustering algorithm that employs
An Efficient Load Balancing Technique for Parallel FMA in Message Passing Environment
 Eighth IEEE Symposium on Parallel and Distributed Processing
, 1996
"... The Nbody simulation has been used extensively in study of the dynamics of galactic systems, fluid, and biomolecules. It is known to be computational bound due to direct force calculation among bodies in the system. The time complexity is O(N 2 ) where N is the number of bodies. Fast multipole al ..."
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Cited by 3 (2 self)
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of load imbalancing among processors which, in turn increases total computational cost. Existing partitioning techniques do not work well due to the tight relationship in the translations of multipole and local expansions when applying to parallel fast multipole algorithm in message passing environment
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