18 citations · 25 across the 9 of their papers we have counts for
3 papers · 1 filter
On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
Shubhendu Trivedi, Brian D. Nord
Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the fiel…
SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks
Sneh Pandya, Purvik Patel, Brian D. Nord +2
Modern neural networks (NNs) often do not generalize well in the presence of a "covariate shift"; that is, in situations where the training and test data distributions differ, but…
DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods
Rebecca Nevin, Aleksandra Ćiprijanović, Brian D. Nord
Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physic…