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Loop series expansion with propagation diagrams
 J. Phy. A: Math. and Theor
"... Abstract. The Bethe approximation is a successful method for approximating partition functions of probabilistic models associated with a graph. Recently, Chertkov and Chernyak derived an interesting formula called “Loop Series Expansion”, which is an expansion of the partition function. The main ter ..."
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Cited by 4 (2 self)
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Abstract. The Bethe approximation is a successful method for approximating partition functions of probabilistic models associated with a graph. Recently, Chertkov and Chernyak derived an interesting formula called “Loop Series Expansion”, which is an expansion of the partition function. The main
Truncating the Loop Series Expansion for Belief Propagation
"... Recently, Chertkov and Chernyak (2006b) derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the belief propagation (BP) solution. By adding correction terms to the BP free energy, one for each “generalized loop ” ..."
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Cited by 1 (0 self)
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Recently, Chertkov and Chernyak (2006b) derived an exact expression for the partition sum (normalization constant) corresponding to a graphical model, which is an expansion around the belief propagation (BP) solution. By adding correction terms to the BP free energy, one for each “generalized loop
Complete discrete 2D Gabor transforms by neural networks for image analysis and compression
, 1988
"... A threelayered neural network is described for transforming twodimensional discrete signals into generalized nonorthogonal 2D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations [lo], [15], which provide a comp ..."
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Cited by 478 (8 self)
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because t e elementary expansion functions are not orthogonal. One orthogonking approach developed for 1D signals by Bastiaans [8], based on biorthonormal expansions, is restricted by constraints on the conjoint sampling rates and invariance of the windowing function, as well as by the fact
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
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Cited by 461 (3 self)
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This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions
 J. MOL. BIOL
, 1997
"... We explore the ability of a simple simulated annealing procedure to assemble nativelike structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the ..."
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Cited by 393 (70 self)
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as the first two terms in a series expansion for the residue probability distributions in the protein database; the decoupling of the distance and environment dependencies of the distributions resolves the major problems with current databasederived scoring functions noted by Thomas and Dill. The simulated
The New Routing Algorithm for the ARPANET
 IEEE TRANSACTIONS ON COMMUNICATIONS
, 1980
"... The new ARPANET routing algorithm is an improvement test results. This paper is a summary of our conclusions only; over the old procedure in that it uses fewer network resources, operates on for more complete descriptions of our research findings, see more realistic estimates of network conditions, ..."
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Cited by 300 (2 self)
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, reacts faster to important our internai reports on this project [3][5]. network changes, and does not suffer from longterm loops or oscillations. In the new procedure, each node in the network maintains a database 11. PROBLEMS WITH THE ORIGINAL ALGORITHM describing the complete network topology
Dataflow Analysis of Array and Scalar References
 International Journal of Parallel Programming
, 1991
"... Given a program written in a simple imperative language (assignment statements, for loops, affine indices and loop limits), this paper presents an algorithm for analyzing the patterns along which values flow as the execution proceeds. For each array or scalar reference, the result is the name an ..."
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Cited by 254 (3 self)
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construction. Keywords dataflow analysis, semantics analysis, array expansion. 1 Introduction It is a well known fact that scientific programs spend most of their running time in executing loops operating on arrays. Hence if a restructuring or optimizing compiler is to do a good job, it must be able to do a
Loop series and Bethe variational bounds in attractive graphical models
, 2008
"... Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field. Methods based on mean field theory are guaranteed to provide lower bounds, whereas certain types of convex relaxations provide upper bounds. In general, loopy belief propaga ..."
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Cited by 33 (0 self)
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field bound, and requires no further work than running BP. We establish these lower bounds using a loop series expansion due to Chertkov and Chernyak, which we show can be derived as a consequence of the tree reparameterization characterization of BP fixed points.
Results 1  10
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9,334