Results 251 to 260 of about 3,905,952 (289)

Neural posterior estimation for population genetics. [PDF]

Genetics
Min J, Ning Y, Pope NS, Baumdicker F, Kern AD.   +4 more
europepmc   +1 more source

Temporal speckle enables probabilistic weights for uncertainty-aware photonic AI

Pernice W, Brückerhoff-Plückelmann F, Borras H, Hulyal SU, Meyer L, Ji X, Hu J, Sun J, Klein B, Ebert F, Dijkstra J, McRae L, Schmidt P, Kippenberg T, Fröning H.   +14 more
europepmc   +1 more source

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Bayesian modelling of neural networks

1999
Bayesian methods deal with explicit assumptions and provide rules for reasoning consistently given those assumptions. Bayesian inferences are subjective in the sense that it is not plausible to reason about data without making assumptions. Bayesian NN learning from data features (i) background information used to select a prior probability distribution
Radu Mutihac, Andres Cicuttin, A. Cerdeira Estrada, Alberto A. Colavita   +3 more
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Bayesian Neural Networks

2021
In this chapter, we introduce the concept of Bayesian Neural Network and motivate the reader, presenting its gains over the classical neural networks. We scrutinize four of the most popular algorithms in the area: Bayes by Backprop, Probabilistic Backpropagation, Monte Carlo Dropout, Variational Adam.
Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, Sérgio Lima Netto   +3 more
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Bayesian Regularization of Neural Networks

2008
Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of a ridge regression.
Frank, Burden, Dave, Winkler
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Bayesian evolution of rich neural networks

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005
In this paper we present a genetic approach that uses a Bayesian fitness function to the design of rich neural network topologies in order to find an optimal domain-specific non-linear function approximator with good generalization performance. Rich neural networks have a feed-forward topology with shortcut connections and arbitrary activation ...
MATTEUCCI, MATTEO, D. Spadoni
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Compiling Bayesian Networks into Neural Networks

1993
The criticism on the usage of Bayesian Networks in expert systems was centered around the claim that the use of probability requires a massive amount of data in the form of conditional probabilities. This paper shows that given information easily obtained from experts, the dependence model and some observations, the conditional probabilities can be ...
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