2 citations · 2 across the 5 of their papers we have counts for
5 papers
Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks
Daniel Ries, Jason Adams, Joshua Zollweg
Neural networks (NN) have become almost ubiquitous with image classification, but in their standard form produce point estimates, with no measure of confidence. Bayesian neural net…
Comparing the quality of neural network uncertainty estimates for classification problems
Daniel Ries, Joshua Michalenko, Tyler Ganter +2
Traditional deep learning (DL) models are powerful classifiers, but many approaches do not provide uncertainties for their estimates. Uncertainty quantification (UQ) methods for DL…
Assessing adult physical activity and compliance with 2008 CDC guidelines using a Bayesian two-part measurement error model
Daniel Ries, Alicia Carriquiry
While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on phys…
The Relationship between Moderate to Vigorous Physical Activity and Metabolic Syndrome: A Bayesian Measurement Error Approach
Daniel Ries, Alicia Carriquiry
Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pr…
Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance
Katherine Goode, Daniel Ries, Joshua Zollweg
Optical spectral-temporal signatures extracted from videos of explosions provide information for identifying characteristics of the corresponding explosive devices. Currently, the…