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Exploiting Tractable Substructures in Intractable Networks
1995
Neural Information Processing Systems
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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 are computationally ...
We develop a refined mean field approximation for inference and learning in probabilistic neural networks. ...
We describe a self-consistent approximation in which tractable substructures are handled by exact computations and only the remaining, intractable parts of the network are handled within mean field theory ...
dblp:conf/nips/SaulJ95
fatcat:ob5bnf5vufgzxfdxl4eypdys3a
Tractable Variational Structures for Approximating Graphical Models
1998
Neural Information Processing Systems
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Within the variational framework for approximating these models, we present two classes of distributions, decimatable Boltzmann Machines and Tractable Belief Networks that go beyond the standard factorized ...
Graphical models provide a broad probabilistic framework with applications in speech recognition (Hidden Markov Models), medical diagnosis (Belief networks) and artificial intelligence (Boltzmann Machines ...
one undirected (decimatable BMs in section (3)) and the other, directed (Tractable Belief Networks in section ( 4 )) . ...
dblp:conf/nips/BarberW98
fatcat:kbrdfuqoqbczth7bh3m4nszv3y
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
[article]
2013
arXiv
pre-print
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In addition, we address the problem of how to choose the graphical structure of the approximating distribution. ...
From the generalised mean field equations we derive rules to simplify the structure of the approximating distribution in advance without affecting the quality of the approximation. ...
It shows and clarifies in which cases the copied potentials of tractable substructures as originally proposed in are optimal. ...
arXiv:1301.3901v1
fatcat:hwcooxop7bgx3fobwznhkxczpe
Approximate Inference by Compilation to Arithmetic Circuits
2010
Neural Information Processing Systems
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Arithmetic circuits (ACs) exploit context-specific independence and determinism to allow exact inference even in networks with high treewidth. ...
In this paper, we introduce the first ever approximate inference methods using ACs, for domains where exact inference remains intractable. ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ARO, DARPA ...
dblp:conf/nips/LowdD10
fatcat:3vugsjcgrnbndi6l7rftmnidwe
Finding the most descriptive substructures in graphs with discrete and numeric labels
2013
Journal of Intelligent Information Systems
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Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. ...
Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. ...
thank Erich Schubert at Ludwig-Maximilians Universität München for assistance with verifying our LOF implementation and providing us with the RP + PINN + LOF implementation ahead of its official release in ...
doi:10.1007/s10844-013-0299-7
fatcat:eqrepgrfi5bi3pwcoiol6ygura
Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels
[chapter]
2013
Lecture Notes in Computer Science
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Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. ...
Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. ...
thank Erich Schubert at Ludwig-Maximilians Universität München for assistance with verifying our LOF implementation and providing us with the RP + PINN + LOF implementation ahead of its official release in ...
doi:10.1007/978-3-642-37382-4_10
fatcat:hvz3v7p3orbwnofe5dv5je7mre
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation
[article]
2021
arXiv
pre-print
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While in the standard formulation NML is intractable, we propose a tractable approximation that allows us to scale our method to high-capacity neural network models. ...
This problem setting emerges in many domains where function evaluation is a complex and expensive process, such as in the design of materials, vehicles, or neural network architectures. ...
Bayesian modeling is generally intractable outside of special choices of the prior and model class Θ where conjugacy can be exploited. ...
arXiv:2102.07970v1
fatcat:zyryacfnsngz5f5eujiew47a5u
Using Combinatorial Optimization within Max-Product Belief Propagation
2006
Neural Information Processing Systems
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In general, the problem of computing a maximum a posteriori (MAP) assignment in a Markov random field (MRF) is computationally intractable. ...
In this paper, we present a new method, called COMPOSE, for exploiting combinatorial optimization for sub-networks within the context of a max-product belief propagation algorithm. ...
Even if MAP inference in the original network is intractable, it may be tractable in each of the sub-networks in the ensemble. ...
dblp:conf/nips/DuchiTEK06
fatcat:6ujyjcldynea5diedelleqxf2m
Mean Field Theory for Sigmoid Belief Networks
[article]
1996
arXiv
pre-print
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Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. ...
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. ...
To facilitate comparisons with similar methods, the results reported in this paper used images that were preprocessed at the University of Toronto. ...
arXiv:cs/9603102v1
fatcat:gnz6ypmhpbhepn4o6pnrlxzvk4
Mean Field Theory for Sigmoid Belief Networks
1996
The Journal of Artificial Intelligence Research
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Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. ...
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. ...
To facilitate comparisons with similar methods, the results reported in this paper used images that were preprocessed at the University o f T oronto. ...
doi:10.1613/jair.251
fatcat:z2cajh2zqbfklhxehyn4b2yzmq
Introduction to the Issue on Stochastic Simulation and Optimization in Signal Processing
2016
IEEE Journal on Selected Topics in Signal Processing
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(ENSEEIHT) de Toulouse, Toulouse, France, in 1989, and the Ph.D. degree from the National Polytechnic Institute Toulouse, Toulouse, France, in 1992. ...
He is currently a professor in the university of Toulouse (ENSEEIHT) and a member of the IRIT laboratory (UMR 5505 of the CNRS). ...
Lindsten et al. present a forward backward-type Rao-Blackwellized particle smoother (RBPS) that is able to exploit the tractable substructure present in these models. ...
doi:10.1109/jstsp.2016.2524963
fatcat:ogalgccjjndihjf2ck6w432hbe
Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-based Diagnosis
[article]
2013
arXiv
pre-print
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In general, precomputing optimal repair policies is intractable. ...
We show how we can exploit a hierarchical system specification to make this approach tractable for large systems. ...
In general, precomputing optimal repair policies is intractable. ...
arXiv:1302.4986v1
fatcat:jt6uf2qjgvforgdmjrrp7wo3za
On tractable cases of Target Set Selection
2012
Social Network Analysis and Mining
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We study the NP-complete TARGET SET SELECTION (TSS) problem occurring in social network analysis. ...
and intractable cases. ...
Now, we describe the data reduction rule that shrinks clique-like substructures. ...
doi:10.1007/s13278-012-0067-7
fatcat:k2q4nk4qnbatnemcvpvm32tqjq
On Tractable Cases of Target Set Selection
[chapter]
2010
Lecture Notes in Computer Science
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We study the NP-complete TARGET SET SELECTION (TSS) problem occurring in social network analysis. ...
and intractable cases. ...
Now, we describe the data reduction rule that shrinks clique-like substructures. ...
doi:10.1007/978-3-642-17517-6_34
fatcat:utuzuybyojah5iljtjbk3j2cuq
Coordinated Passive Beamforming for Distributed Intelligent Reflecting Surfaces Network
[article]
2020
arXiv
pre-print
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In this paper, we propose a distributed IRS-empowered communication network architecture, where multiple source-destination pairs communicate through multiple distributed IRSs. ...
Intelligent reflecting surface (IRS) is a proposing technology in 6G to enhance the performance of wireless networks by smartly reconfiguring the propagation environment with a large number of passive ...
spectral efficiency and energy efficiency in dense wireless networks [13] . ...
arXiv:2002.05915v1
fatcat:n3oj3txjyjay7dsetdowa23kn4
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