Results 31 to 40 of about 118,631 (311)
Open Mathematics, 2018 The problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to
Wang Jingyun, Liu Sanyang
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Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks
Entropy, 2021 We make two related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC–Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of partially ...
Felix Biggs, Benjamin Guedj
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Testing Bayesian Networks [PDF]
IEEE Transactions on Information Theory, 2020 This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node.
Clément L. Canonne +3 more
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CoRR, 2023 This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution.
Alex Graves +3 more
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Balanced Quantum-Like Bayesian Networks
Entropy, 2020 Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions.
Andreas Wichert +2 more
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]
Pesquisa Agropecuária Brasileira, 2017 : The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva +9 more
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Loop amplitudes from precision networks
SciPost Physics Core, 2023 Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably.
Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn
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CoRR, 2018 This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling.
Vikram Mullachery +2 more
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Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks
Applied Artificial Intelligence, 2019 Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot ...
Messaouda Fareh
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Bayesian generalized network design [PDF]
Theoretical Computer Science, 2020 25 pages, 0 figure. An extended abstract of this paper is to appear in the 27th Annual European Symposium on Algorithms (ESA 2019)
Yuval Emek +3 more
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