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Hardware-efficient belief propagation
2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009Loopy belief propagation (BP) is an effective solution for assigning labels to the nodes of a graphical model such as the Markov random field (MRF), but it requires high memory, bandwidth, and computational costs. Furthermore, the iterative, pixel-wise, and sequential operations of BP make it difficult to parallelize the computation.
Chia-Kai Liang +4 more
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Convex combination belief propagation
Applied Mathematics and Computation, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anna Grim, Pedro F. Felzenszwalb
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Regularized Gaussian belief propagation
Statistics and Computing, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Francois Kamper +3 more
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Noisy belief propagation decoder
2014 48th Asilomar Conference on Signals, Systems and Computers, 2014This paper analyzes the fundamental performance limits of an LDPC Belief Propagation (BP) decoder implemented on noisy hardware and proposes a robust decoder implementation to improve the resilience to hardware errors. Assuming that the effects of hardware noise in various computational units, i.e., variable nodes and check nodes, can be approximated ...
Chu-Hsiang Huang +2 more
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Structured Belief Propagation for NLP
Tutorials, 2014Statistical natural language processing relies on probabilistic models of linguistic structure. More complex models can help capture our intuitions about language, by adding linguistically meaningful interactions and latent variables. However, inference and learning in the models we want often poses a serious computational challenge. Belief propagation
Matthew Gormley 0001, Jason Eisner
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Noise predictive belief propagation
IEEE International Conference on Communications, 2005. ICC 2005. 2005, 2005We introduce iterative noise whitening for belief propagation (BP) based channel detectors over intersymbol interference (ISI) channels with correlated noise. Called noise predictive belief propagation (NPBP), the new scheme iteratively whitens the noise samples by modifying the edge probability computation of the BP algorithm.
Mustafa Nazmi Kaynak +2 more
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2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017
This paper considers the problem of decentralized, cooperative, and dynamic self-localization in wireless sensor networks. In particular, we are interested in a restrictive but very realistic scenario where few anchors are deployed and each anchor whose location is priori known may only communicate with very few agents (e.g.
Yang Song 0012 +3 more
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This paper considers the problem of decentralized, cooperative, and dynamic self-localization in wireless sensor networks. In particular, we are interested in a restrictive but very realistic scenario where few anchors are deployed and each anchor whose location is priori known may only communicate with very few agents (e.g.
Yang Song 0012 +3 more
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2017
We propose a new approximate inference algorithm for graphical models, tensor belief propagation, based on approximating the messages passed in the junction tree algorithm. Our algorithm represents the potential functions of the graphical model and all messages on the junction tree compactly as mixtures of rank-1 tensors.
Wrigley, Andrew, Lee, Wee Sun, Ye, Nan
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We propose a new approximate inference algorithm for graphical models, tensor belief propagation, based on approximating the messages passed in the junction tree algorithm. Our algorithm represents the potential functions of the graphical model and all messages on the junction tree compactly as mixtures of rank-1 tensors.
Wrigley, Andrew, Lee, Wee Sun, Ye, Nan
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Relaxed Gaussian Belief Propagation
2012 IEEE International Symposium on Information Theory Proceedings, 2012The Gaussian Belief Propagation (GaBP) algorithm executed on Gaussian Markov Random Fields can take a large number of iterations to converge if the inverse covariance matrix of the underlying Gaussian distribution is ill-conditioned and weakly diagonally dominant. Such matrices can arise from many practical problem domains.
Yousef El-Kurdi +2 more
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Motion Estimation via Belief Propagation
14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007We present a probabilistic model for motion estimation in which motion characteristics are inferred on the basis of a finite mixture of motion models. The model is graphically represented in the form of a pairwise Markov Random Field network upon which a Loopy Belief Propagation algorithm is exploited to perform inference.
Giuseppe Boccignone +3 more
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