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A New Class of Upper Bounds on the Log Partition Function
 In Uncertainty in Artificial Intelligence
, 2002
"... Bounds on the log partition function are important in a variety of contexts, including approximate inference, model fitting, decision theory, and large deviations analysis [11, 5, 4]. We introduce a new class of upper bounds on the log partition function, based on convex combinations of distribution ..."
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Cited by 220 (33 self)
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Bounds on the log partition function are important in a variety of contexts, including approximate inference, model fitting, decision theory, and large deviations analysis [11, 5, 4]. We introduce a new class of upper bounds on the log partition function, based on convex combinations
A New Class of Upper Bounds on the Log Partition Function
 In Uncertainty in Artificial Intelligence
, 2002
"... Bounds on the log partition function are important in a variety of contexts, including approximate inference, model tting, decision theory, and large deviations analysis [11, 5, 4]. We introduce a new class of upper bounds on the log partition function, based on convex combinations of distrib ..."
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Bounds on the log partition function are important in a variety of contexts, including approximate inference, model tting, decision theory, and large deviations analysis [11, 5, 4]. We introduce a new class of upper bounds on the log partition function, based on convex combinations
A Convex Upper Bound on the LogPartition Function for Binary Graphical Models
"... We consider the problem of bounding from above the logpartition function corresponding to secondorder Ising models for binary distributions. We introduce a new bound, the cardinality bound, which can be computed via convex optimization. The corresponding error on the logpartition function is bound ..."
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Cited by 3 (0 self)
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We consider the problem of bounding from above the logpartition function corresponding to secondorder Ising models for binary distributions. We introduce a new bound, the cardinality bound, which can be computed via convex optimization. The corresponding error on the logpartition function
Strong cluster properties and geometric expansion of the logpartition function of the Ginibre gas
"... We consider the geometric expansion of the logpartition function of a system of interacting Brownian loops in a bounded domain. This expansion generalizes the corresponding one for a classical gas due to one of the authors (see [10]). It can also be considered as a natural generalization of a famou ..."
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We consider the geometric expansion of the logpartition function of a system of interacting Brownian loops in a bounded domain. This expansion generalizes the corresponding one for a classical gas due to one of the authors (see [10]). It can also be considered as a natural generalization of a
Sequential cavity method for computing limits of the logpartition function for lattice models
, 2009
"... ..."
Counting Without Sampling: Asymptotics of the LogPartition Function for Certain Statistical Physics Models
, 2006
"... In this article we propose new methods for computing the asymptotic value for the logarithm of the partition function (free energy) for certain statistical physics models on certain type of finite graphs, as the size of the underlying graph goes to infinity. The two models considered are the hard ..."
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Cited by 29 (6 self)
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In this article we propose new methods for computing the asymptotic value for the logarithm of the partition function (free energy) for certain statistical physics models on certain type of finite graphs, as the size of the underlying graph goes to infinity. The two models considered are the hard
Acknowledgement Research supported in part by NSF grant DMS0625371. A Convex Upper Bound on the LogPartition Function for Binary Graphical Models ∗
, 2007
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Cluster Ensembles  A Knowledge Reuse Framework for Combining Multiple Partitions
 Journal of Machine Learning Research
, 2002
"... This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse&ap ..."
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Cited by 589 (21 self)
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(consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into metaclusters which then compete for each object to determine the combined
LogP: Towards a Realistic Model of Parallel Computation
, 1993
"... A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding developme ..."
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Cited by 562 (15 self)
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development of techniques that yield performance across a range of current and future parallel machines. This paper offers a new parallel machine model, called LogP, that reflects the critical technology trends underlying parallel computers. It is intended to serve as a basis for developing fast, portable
Results 1  10
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