Results 141 to 150 of about 1,107,982 (188)

Calibrating deep classifiers with dynamic confidence propagation and adaptive normalization. [PDF]

Sci Rep
He P, Fu W, Wang L, Fu T.
europepmc   +1 more source

Cyber Defense Effectiveness Evaluation for ICS Under Uncertainty: A Dynamic Bayesian Network Approach with Information Entropy. [PDF]

Entropy (Basel)
Kang R, Zhang Z, Zhang X, Chen J, Xu R, Zhang Y, Rao Z.   +6 more
europepmc   +1 more source

Functional neurological disorder, suggestion and virtual reality: neuroanatomical and computational perspectives. [PDF]

Philos Trans R Soc Lond B Biol Sci
Deeley Q.
europepmc   +1 more source

The Peptonizer2000: Bringing Confidence to Metaproteomics. [PDF]

J Proteome Res
Holstein T, Verschaffelt P, Van Den Bossche T, Van de Vyver S, Martens L, Mesuere B, Muth T.   +6 more
europepmc   +1 more source

A Sensor-Centric Survey of Autonomous Driving: Integrating Measurement Physics, Uncertainty Modeling, and Safety-Critical Multi-Sensor Fusion. [PDF]

Sensors (Basel)
Iqbal U, Massoud A, Noureldin A.
europepmc   +1 more source

Temporal-difference valence-partitioned Bayesian brains work out whether others are caring or uncaring

Moutoussis M, Frydman Laiter A, Griem J, Erfanian Delavar D, Nolte T, Fonagy P, Montague R, Litvak V.   +7 more
europepmc   +1 more source

Cooperative Localization Using Posterior Linearization Belief Propagation [PDF]

IEEE Transactions on Vehicular Technology, 2018
This paper presents the posterior linearization belief propagation (PLBP) algorithm for cooperative localization in wireless sensor networks with nonlinear measurements. PLBP performs two steps iteratively: linearization and belief propagation.
Angel F García-Fernández, Simo Särkkä, Lennart Svensson   +2 more
exaly   +2 more sources

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Dithered Belief Propagation Decoding

IEEE Transactions on Communications, 2012
We 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.
Saied Hemati, François Leduc-Primeau, Shie Mannor   +2 more
exaly   +3 more sources

Hardware-efficient belief propagation

2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009
Loopy 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, Chao-Chung Cheng, Yen-Chieh Lai, Liang-Gee Chen, Homer H. Chen   +4 more
openaire   +1 more source