Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

20 February 2023

Papers citing "Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles"

5 / 5 papers shown

Title
Empowering Bayesian Neural Networks with Functional Priors through Anchored Ensembling for Mechanics Surrogate Modeling Applications Javad Ghorbanian Nicholas Casaprima Audrey Olivier 28 0 0 08 Sep 2024
Higher order quantum reservoir computing for non-intrusive reduced-order models Saloua Naama R. Maulik 16 0 0 31 Jul 2024
Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning Tyler Chang Andrew Gillette R. Maulik 31 2 0 04 Apr 2024
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task Sunwoong Yang K. Yee UQCV 32 5 0 28 Mar 2023
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016