Results 31 to 40 of about 160,175 (304)
Cycle-based Cluster Variational Method for Direct and Inverse Inference [PDF]
, 2016 We elaborate on the idea that loop corrections to belief propagation could be dealt with in a systematic way on pairwise Markov random fields, by using the elements of a cycle basis to define region in a generalized belief propagation setting. The region
A Decelle +42 more
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Homography-guided stereo matching for wide-baseline image interpolation
Computational Visual Media, 2021 Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose
Yuan Chang +3 more
doaj +1 more source
Low Complexity Early Stopping Belief Propagation Decoder for Polar Codes
IEEE AccessBelief propagation is one of low latency decoding algorithms for polar codes but it requires relatively high decoding complexity due to its inherent iterative decoding nature.
Chungsu Lee +3 more
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, 2006 We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish convergence, characterize the convergence rate for regular graphs, and demonstrate that the protocol exhibits better scaling ...
Moallemi, Ciamac C., Van Roy, Benjamin
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Belief Propagation on replica symmetric random factor graph models [PDF]
, 2016 According to physics predictions, the free energy of random factor graph models that satisfy a certain "static replica symmetry" condition can be calculated via the Belief Propagation message passing scheme [Krzakala et al., PNAS 2007].
Coja-Oghlan, Amin, Perkins, Will
core +4 more sources
A Simple Scheme for Belief Propagation Decoding of BCH and RS Codes in Multimedia Transmissions
International Journal of Digital Multimedia Broadcasting, 2008 Classic linear block codes, like Bose-Chaudhuri-Hocquenghem (BCH) and Reed-Solomon (RS) codes, are widely used in multimedia transmissions, but their soft-decision decoding still represents an open issue.
Marco Baldi, Franco Chiaraluce
doaj +1 more source
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
doaj +1 more source
Belief Propagation Through Provenance Graphs [PDF]
, 2018 Provenance of food describes food, the processes in food transformation, and the food operators from the source to consumption; modelling the history food. In processing food, the risk of contamination increases if food is treated inappropriately.
Batlajery, Belfrit Victor +3 more
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Belief Propagation as Diffusion [PDF]
, 2021 We introduce novel belief propagation algorithms to estimate the marginals of a high dimensional probability distribution. They involve natural (co)homological constructions relevant for a localised description of statistical systems.
openaire +2 more sources
Low-Complexity Stochastic Generalized Belief Propagation
, 2016 The generalized belief propagation (GBP), introduced by Yedidia et al., is an extension of the belief propagation (BP) algorithm, which is widely used in different problems involved in calculating exact or approximate marginals of probability ...
Haddadpour, Farzin +2 more
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