Results 241 to 250 of about 1,067,198 (282)
Some of the next articles are maybe not open access.
Convex combination belief propagation
Applied Mathematics and Computation, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anna Grim, Pedro F. Felzenszwalb
openaire +2 more sources
Dithered Belief Propagation Decoding
IEEE Transactions on Communications, 2012We introduce two dithered belief propagation decoding algorithms to lower the error floor with a minimal hardware overhead. One of the algorithms can additionally improve the decoding performance in the waterfall region using a large iteration limit but with a negligible increase in the average time complexity.
François Leduc-Primeau +3 more
openaire +2 more sources
Regularized Gaussian belief propagation
Statistics and Computing, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Francois Kamper +3 more
openaire +1 more source
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
openaire +2 more sources
Conditioned Belief Propagation Revisited
2014Belief Propagation (BP) applied to cyclic problems is a well known approximate inference scheme for probabilistic graphical models. To improve the accuracy of BP, a divide-and-conquer approach termed Conditioned Belief Propagation (CBP) has been proposed in the literature.
Thomas Geier +2 more
openaire +2 more sources
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
openaire +3 more sources
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
openaire +3 more sources
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
openaire +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +1 more source