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23
Exploiting Tractable Substructures in Intractable Networks
 Advances in Neural Information Processing Systems 8
, 1995
"... We develop a refined mean field approximation for inference and learning in probabilistic neural networks. Our mean field theory, unlike most, does not assume that the units behave as independent degrees of freedom; instead, it exploits in a principled way the existence of large substructures that a ..."
Abstract

Cited by 117 (12 self)
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We develop a refined mean field approximation for inference and learning in probabilistic neural networks. Our mean field theory, unlike most, does not assume that the units behave as independent degrees of freedom; instead, it exploits in a principled way the existence of large substructures
Fixedparameter tractability, definability, and model checking
 SIAM JOURNAL ON COMPUTING
, 2001
"... In this article, we study parameterized complexity theory from the perspective of logic, or more specifically, descriptive complexity theory. We propose to consider parameterized modelchecking problems for various fragments of firstorder logic as generic parameterized problems and show how this ap ..."
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Cited by 37 (13 self)
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this approach can be useful in studying both fixedparameter tractability and intractability. For example, we establish the equivalence between the modelchecking for existential firstorder logic, the homomorphism problem for relational structures, and the substructure isomorphism problem. Our main
Computational experience with generic decomposition using the DIP framework
"... Decomposition methods are techniques for exploiting the tractable substructures of an integer program in order to obtain improved solution techniques. In particular, the fundamental idea is to exploit our ability to either optimize over and/or separate from the convex hull of solutions to a given re ..."
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Cited by 1 (0 self)
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Decomposition methods are techniques for exploiting the tractable substructures of an integer program in order to obtain improved solution techniques. In particular, the fundamental idea is to exploit our ability to either optimize over and/or separate from the convex hull of solutions to a given
Decomposition Methods
, 2010
"... Decomposition methods are techniques for exploiting the tractable substructures of an integer program in order to obtain improved solution procedures. In particular, the goal is to derive improved methods of bounding the optimal solution value, which can then be used to drive a branchandbound algor ..."
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Decomposition methods are techniques for exploiting the tractable substructures of an integer program in order to obtain improved solution procedures. In particular, the goal is to derive improved methods of bounding the optimal solution value, which can then be used to drive a branch
Expectation consistent approximate inference
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... We propose a novel framework for approximations to intractable probabilistic models which is based on a free energy formulation. The approximation can be understood from replacing an average over the original intractable distribution with a tractable one. It requires two tractable probability distri ..."
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Cited by 33 (5 self)
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other methods. We test the framework on toy benchmark problems for binary variables on fully connected graphs and 2D grids and compare with other methods, such as loopy belief propagation. Good performance is already achieved by using single nodes as tractable substructures. Significant improvements
Expectation consistent approximate inference
 Journal of Machine Learning Research
, 2005
"... We propose a novel framework for approximations to intractable probabilistic models which is based on a free energy formulation. The approximation can be understood as replacing an average over the original intractable distribution with a tractable one. It requires two tractable probability distribu ..."
Abstract

Cited by 1 (0 self)
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other methods. We test the framework on toy benchmark problems for binary variables on fully connected graphs and 2D grids and compare with other methods, such as loopy belief propagation. Good performance is already achieved by using single nodes as tractable substructures. Significant improvements
TO APPEAR IN APJ Preprint typeset using LATEX style emulateapj v. 10/09/06 A NEW CHANNEL FOR DETECTING DARK MATTER SUBSTRUCTURE IN GALAXIES: GRAVITATIONAL LENS TIME DELAYS
, 805
"... We show that dark matter substructure in galaxyscale halos perturbs the time delays between images in strong gravitational lens systems. The variance of the effect depends on the subhalo mass function, scaling as the product of the substructure mass fraction and a characteristic mass of subhalos (n ..."
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We show that dark matter substructure in galaxyscale halos perturbs the time delays between images in strong gravitational lens systems. The variance of the effect depends on the subhalo mass function, scaling as the product of the substructure mass fraction and a characteristic mass of subhalos
MARGINALIZATION OF STATIC OBSERVATION PARAMETERS IN A RAOBLACKWELLIZED PARTICLE FILTER WITH APPLICATION TO SEQUENTIAL BLIND SPEECH DEREVERBERATION
"... Enhancement of an unknown signal from distorted observations is an extremely important Engineering problem. In addition to noise, the observation space often contains a degrading filter component. A typical example is blind speech enhancement, where a reverberant channel between a stationary source ..."
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Cited by 1 (1 self)
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sampling of static components leads to particle impoverishment as a dynamic is implicitly enforced on the parameters. To circumvent this issue, this paper proposes a novel approach by exploiting analytically tractable substructures of the state space to marginalize static components, facilitating separate
Improvements In Speech Understanding Accuracy Through The Integration Of Hierarchical Linguistic, Prosodic, And Phonological Constraints In The Jupiter Domain
 in Proc. ICSLP
, 1998
"... This paper explores some issues in designing conversational systems with integrated higher level constraints. We experiment with a configuration that combines a contextdependent acoustic frontend, using MIT's SUMMIT recognizer, with ANGIE, a hierarchical framework that models word substructur ..."
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Cited by 14 (3 self)
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substructure and phonological processes, and with TINA, a trainable probabilistic natural language (NL) model. Working in the Jupiter weather domain, we develop a computationally tractable system which incorporates higher level linguistic, prosodic and phonological constraints together in the second of a two
Decomposition theorems and modelchecking for the modal µcalculus
 Joint Meeting of the TwentyThird EACSL Annual Conference on Computer Science Logic (CSL) and the TwentyNinth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), CSLLICS ’14
"... Abstract—We prove a general decomposition theorem for the modal µcalculus Lµ in the spirit of Feferman and Vaught’s theorem for disjoint unions. In particular, we show that if a structure (i.e., transition system) is composed of two substructures M1 and M2 plus edges from M1 to M2, then the formu ..."
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Cited by 2 (0 self)
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, then the formulas true at a node in M only depend on the formulas true in the respective substructures in a sense made precise below. As a consequence we show that the modelchecking problem for Lµ is fixedparameter tractable (fpt) on classes of structures of bounded Kellywidth or bounded DAGwidth. As far as we
Results 1  10
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