most citedSparse Bayesian neural networks for regression: Tackling overfitting and computational challenges in uncertainty quantification

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cs.CE2023★ 1 cited

Sparse Bayesian neural networks for regression: Tackling overfitting and computational challenges in uncertainty quantification

Nastaran Dabiran, Brandon Robinson, Rimple Sandhu +3

Neural networks (NNs) are primarily developed within the frequentist statistical framework. Nevertheless, frequentist NNs lack the capability to provide uncertainties in the predic…

cs.CE2023

Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations

Nastaran Dabiran, Brandon Robinson, Rimple Sandhu +4

Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct…