Results 31 to 40 of about 16,794 (295)
Belief Propagation for Linear Programming [PDF]
2013 IEEE International Symposium on Information Theory, 2013 Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to solve a special class of Linear Programming (LP) problems. For this class of problems, MAP inference can be stated as an
Andrew E. Gelfand +2 more
openaire +2 more sources
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
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
Belief Propagation Neural Networks
CoRR, 2020 Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems. More general counting variants of these problems, however, are still largely solved with hand-crafted solvers. To bridge this gap, we introduce belief propagation neural networks (BPNNs), a class of parameterized operators that operate on ...
Jonathan Kuck +6 more
openaire +3 more sources
Dependency parsing by belief propagation [PDF]
Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08, 2008 We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient.
David A. Smith, Jason Eisner
openaire +2 more sources
Differentiable Nonparametric Belief Propagation
CoRR, 2021 12 pages, 9 ...
Anthony Opipari +5 more
openaire +2 more sources
The Algebra of Multi-Agent Dynamic Belief Revision
, 2005 We refine our algebraic axiomatization in [8,9] of epistemic actions and epistemic update (notions defined in [5,6] using Kripke-style semantics), to incorporate a mechanism for dynamic belief revision in a multi-agent setting.
Sadrzadeh, Mehrnoosh +8 more
core +1 more source
Graph Belief Propagation Networks
CoRR, 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 ...
Junteng Jia +3 more
openaire +2 more sources
FEBS Letters, EarlyView.The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source
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