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True amplitude oneway propagation in heterogeneous media
, 804
"... apport de recherche ISSN 02496399 ISRN INRIA/RR????FR+ENGTrue amplitude oneway propagation in heterogeneous media ..."
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apport de recherche ISSN 02496399 ISRN INRIA/RR????FR+ENGTrue amplitude oneway propagation in heterogeneous media
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 484 (8 self)
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with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. tree
Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms
 IEEE Transactions on Information Theory
, 2005
"... Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
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Cited by 585 (13 self)
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Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems
Shape modeling with front propagation: A level set approach
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods and over ..."
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Cited by 808 (20 self)
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than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian to model propagating solidhiquid interfaces with curvaturedependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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propagation converges, it gives a surprisingly good ap proximation to the correct marginals. Since the dis tinction between convergence and oscillation is easy to make after a small number of iterations, this may sug gest a way of checking whether loopy propagation is appropriate for a given problem. Acknowl
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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belief propagation; a way of
applying RaoBlackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization
and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main
A Boussinesq system for twoway propagation of interfacial waves
, 709
"... The theory of internal waves between two layers of immiscible fluids is important both for its applications in oceanography and engineering, and as a source of interesting mathematical model equations that exhibit nonlinearity and dispersion. A Boussinesq system for twoway propagation of interfacia ..."
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Cited by 9 (0 self)
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The theory of internal waves between two layers of immiscible fluids is important both for its applications in oceanography and engineering, and as a source of interesting mathematical model equations that exhibit nonlinearity and dispersion. A Boussinesq system for twoway propagation
Recommendation G.114 MEAN ONEWAY PROPAGATION TIME
, 1988
"... d not be used except under the most exceptional circumstances. Until such time as additional, significant information permits Administrations to make a firmer determination of acceptable delay limits, they should take full account of the documents referred to under References in selecting, from alt ..."
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alternatives, plans involving delays in range b) above. Note 1  The above values refer only to the propagation time between two subscribers. However, for other purposes (e.g. in Recommendation G.131) the mean oneway propagation time of an echo path is to be estimated. The values in 2 may be used
Variational algorithms for approximate Bayesian inference
, 2003
"... The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents ..."
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Cited by 440 (9 self)
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The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents
Automatic Programming of Behaviorbased Robots using Reinforcement Learning
, 1991
"... This paper describes a general approach for automatically programming a behaviorbased robot. New behaviors are learned by trial and error using a performance feedback function as reinforcement. Two algorithms for behavior learning are described that combine Q learning, a well known scheme for propa ..."
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Cited by 368 (17 self)
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for propagating reinforcement values temporally across actions, with statistical clustering and Hamming distance, two ways of propagating reinforcement values spatially across states. A real behaviorbased robot called OBELIX is described that learns several component behaviors in an example task involving
Results 1  10
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