Results 61 to 70 of about 157,133 (176)
Graph Belief Propagation Networks
, 2021 With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem. Graph neural networks are a recent class of easy-to-train and accurate methods for this problem that map the features in the neighborhood of a node to its label, but they ignore label correlation ...
Jia, Junteng +3 more
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Deep Learning Methods for Improved Decoding of Linear Codes
, 2018 The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large ...
Beery, Yair +5 more
core +1 more source
Derived dynamic scheduling for belief propagation decoding of LDPC codes
Electronics LettersAmong numerous dynamic scheduling strategies for low‐density parity‐check codes, many of them are of high complexity due to repetitive computation and ordering of belief residuals.
Xiaotian Xu, Hua Zhou, Jiayi Zhao
doaj +1 more source
DNBP: Differentiable Nonparametric Belief Propagation
ACM / IMS Journal of Data ScienceWe present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm. Existing nonparametric belief propagation methods rely on domain-specific features encoded in the probabilistic factors of a graphical model.
Anthony Opipari +5 more
openaire +3 more sources
Distributed Convergence Verification for Gaussian Belief Propagation
, 2017 Gaussian belief propagation (BP) is a computationally efficient method to approximate the marginal distribution and has been widely used for inference with high dimensional data as well as distributed estimation in large-scale networks.
Du, Jian +2 more
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Low Complexity Belief Propagation Polar Code Decoders
, 2015 Since its invention, polar code has received a lot of attention because of its capacity-achieving performance and low encoding and decoding complexity.
Abbas, Syed Mohsin +3 more
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Loopy belief propagation and probabilistic image processing [PDF]
, 2003 Estimation of hyperparameters by maximization of the marginal likelihood in probabilistic image processing is investigated by using the cluster variation method.
Inoue, J., Tanaka, K., Titterington, M.
core
Fractional Belief Propagation [PDF]
, 2003 Contains fulltext : 100943.pdf (Author’s version preprint ) (Open Access)
Wiegerinck, W.A.J.J., Heskes, T.
openaire
Anytime Exact Belief Propagation
, 2017 Statistical Relational Models and, more recently, Probabilistic Programming, have been making strides towards an integration of logic and probabilistic reasoning. A natural expectation for this project is that a probabilistic logic reasoning algorithm reduces to a logic reasoning algorithm when provided a model that only involves 0-1 probabilities ...
Ferreira, Gabriel Azevedo +3 more
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Polynomial Linear Programming with Gaussian Belief Propagation
, 2008 Interior-point methods are state-of-the-art algorithms for solving linear programming (LP) problems with polynomial complexity. Specifically, the Karmarkar algorithm typically solves LP problems in time O(n^{3.5}), where $n$ is the number of unknown ...
Bickson, Danny +3 more
core +2 more sources