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Parameterized Weighted Containment
"... Abstract. Partiallyspecified systems and specifications are used in formal methods such as stepwise design and query checking. The goal of these methods is to explore the design or refine it by examining possible completions of the missing information. Existing methods consider a setting in which t ..."
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Cited by 1 (1 self)
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reasoning. Technically, every transition in a weighted automaton A has a cost, and the value A assigns to a finite word w is the sum of the costs on the transitions participating in the most expensive accepting run of A on w. We study parameterized weighted containment: given three weighted automata A, B
LightWeight Containers
"... This development provides a framework for container types like sets and maps such that generated code implements these containers with different (efficient) data structures. Thanks to type classes and refinement during code generation, this lightweight approach can seamlessly replace Isabelle’s def ..."
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This development provides a framework for container types like sets and maps such that generated code implements these containers with different (efficient) data structures. Thanks to type classes and refinement during code generation, this lightweight approach can seamlessly replace Isabelle’s
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 816 (39 self)
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in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a firstorder formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently
A distributed algorithm for minimumweight spanning trees
, 1983
"... A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm and exchange ..."
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Cited by 435 (3 self)
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and exchange messages with neighbors until the tree is constructed. The total number of messages required for a graph of N nodes and E edges is at most 5N log2N + 2E, and a message contains at most one edge weight plus log28N bits. The algorithm can be initiated spontaneously at any node or at any subset
Yago: A Core of Semantic Knowledge
 IN PROC. OF WWW ’07
, 2007
"... We present YAGO, a lightweight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains roughly 900,000 entities and 5,000,000 facts. This includes the IsA hierarchy as well as nontaxonomic relations between entities (such as hasWonPrize ..."
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Cited by 504 (66 self)
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We present YAGO, a lightweight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains roughly 900,000 entities and 5,000,000 facts. This includes the IsA hierarchy as well as nontaxonomic relations between entities (such as has
Estimating Wealth Effects without Expenditure Data— or Tears
 Policy Research Working Paper 1980, The World
, 1998
"... Abstract: We use the National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993 to estimate the relationship between household wealth and the probability a child (aged 6 to 14) is enrolled in school. A methodological difficulty to overcome is that the NFHS, modeled closely ..."
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Cited by 871 (16 self)
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closely on the Demographic and Health Surveys (DHS), measures neither household income nor consumption expenditures. As a proxy for longrun household wealth we construct a linear index from a set of asset indicators using principal components analysis to derive the weights. This “asset index ” is robust
Similarity of Color Images
, 1995
"... We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 , L 2 , or L1 distance between two cumulative color histograms can be used to define a similarity mea ..."
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Cited by 495 (2 self)
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of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which
Efficient Implementation of Weighted ENO Schemes
, 1995
"... In this paper, we further analyze, test, modify and improve the high order WENO (weighted essentially nonoscillatory) finite difference schemes of Liu, Osher and Chan [9]. It was shown by Liu et al. that WENO schemes constructed from the r th order (in L¹ norm) ENO schemes are (r +1) th order accur ..."
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Cited by 412 (38 self)
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and entropy instead of the characteristic values to simplify the costly characteristic procedure. The resulting WENO schemes are about twice as fast as the WENO schemes using the characteristic decompositions to compute weights, and work well for problems which donot contain strong shocks or strong reflected
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 451 (0 self)
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network relative to the problem domain, the bigger the possibility that the weight space contains long ravines characterized by sharp curvature. While BP is inefficient on these ravine phenomena, it is shown that SCG handles them effectively.
Simple statistical gradientfollowing algorithms for connectionist reinforcement learning
 Machine Learning
, 1992
"... Abstract. This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinfor ..."
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Cited by 449 (0 self)
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Abstract. This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected
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