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

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

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.CE2022

Domain Decomposition of Stochastic PDEs: Development of Probabilistic Wirebasket-based Two-level Preconditioners

Ajit Desai, Mohammad Khalil, Chris L. Pettit +2

Realistic physical phenomena exhibit random fluctuations across many scales in the input and output processes. Models of these phenomena require stochastic PDEs. For three-dimensio…