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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 +3 more
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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 +3 more
openaire +1 more source
Bayesian Regularization of Neural Networks
2008Bayesian 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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Biological Cybernetics, 1989
A neural network that uses the basic Hebbian learning rule and the Bayesian combination function is defined. Analogously to Hopfield's neural network, the convergence for the Bayesian neural network that asynchronously updates its neurons' states is proved.
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A neural network that uses the basic Hebbian learning rule and the Bayesian combination function is defined. Analogously to Hopfield's neural network, the convergence for the Bayesian neural network that asynchronously updates its neurons' states is proved.
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Dynamic Bayesian Neural Networks
2020We define an evolving in time Bayesian neural network called a Hidden Markov neural network. The weights of a feed-forward neural network are modelled with the hidden states of a Hidden Markov model, whose observed process is given by the available data.
Rimella, Lorenzo, Whiteley, Nick
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Bayesian Confidence Propagation Neural Network
Drug Safety, 2007A Bayesian confidence propagation neural network (BCPNN)-based technique has been in routine use for data mining the 3 million suspected adverse drug reactions (ADRs) in the WHO database of suspected ADRs of as part of the signal-detection process since 1998.
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Neural network classification: a Bayesian interpretation
IEEE Transactions on Neural Networks, 1990The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical ...
exaly +3 more sources