A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows

13 January 2023

Papers citing "A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows"

2 / 2 papers shown

Title
A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty Pratyush Kumar Singh Kathryn A. Farrell-Maupin D. Faghihi 32 6 0 13 Mar 2024
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015