Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance. - Katherine Goode; Daniel Ries; Joshua Zollweg (CoRR, 2020)

Hyperspectral Image Target Detection Using Deep Ensembles for Robust Uncertainty Quantification. - Rajeev Sahay; Daniel Ries; Joshua D. Zollweg; Christopher G. Brinton (ACSCC, 2021)

Comparing the quality of neural network uncertainty estimates for classification problems. - Daniel Ries; Joshua Michalenko; Tyler Ganter; Rashad Imad-Fayez Baiyasi; Jason Adams (ICMLA, 2022)

Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks. - Daniel Ries; Jason Adams; Joshua Zollweg (CoRR, 2023)

A Framework for Inverse Prediction Using Functional Response Data. - Daniel Ries; Adah Zhang; J. Derek Tucker; Kurtis Shuler; Madeline Ausdemore (J. Comput. Inf. Sci. Eng., 2023)

Characterizing climate pathways using feature importance on echo state networks. - Katherine Goode; Daniel Ries; Kellie McClernon (Stat. Anal. Data Min., 2024)

Elastic Bayesian Model Calibration. - Devin Francom; J. Derek Tucker; Gabriel Huerta; Kurtis Shuler; Daniel Ries (SIAM/ASA J. Uncertain. Quantification, 2025)

An Explainable Pipeline for Machine Learning with Functional Data. - Katherine Goode; J. Derek Tucker; Daniel Ries; Heike Hofmann (CoRR, 2025)

Regression Recalibration by Learning PIT Map Values. - Christopher Qian; Daniel Ries; Feng Liang 002; Jason Adams (Technometrics, 2025)