Quantifying uncertainty in deep learning binary classification with discrete noise in inputs for risk-based decision making
- 14 July 2025
- journal article
- Published by Taylor & Francis in IISE Transactions
- Vol. 58 (4) , 426-442
Abstract
No abstract availableAll Related Versions
Funding Information
- National Institute of Health Sciences
- NIH Grant (R01AI137681)
- National Science Foundation
- NSF grant (OIA-1946391)
This publication has 22 references indexed in Scilit:
- A survey of uncertainty in deep neural networksArtificial Intelligence Review, 2023
- Optimizing model parameters of artificial neural networks to predict vehicle emissionsAtmospheric Environment, 2022
- Aleatoric Uncertainty for Errors-in-Variables Models in Deep RegressionNeural Processing Letters, 2022
- A review of uncertainty quantification in deep learning: Techniques, applications and challengesInformation Fusion, 2021
- DropConnect is effective in modeling uncertainty of Bayesian deep networksScientific Reports, 2021
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methodsMachine Learning, 2021
- Model predictive control with active learning under model uncertainty: Why, when, and howAIChE Journal, 2018
- Diagnosis of active tuberculosis disease: From microscopy to molecular techniquesJournal of Clinical Tuberculosis and Other Mycobacterial Diseases, 2016
- Diagnostic accuracy of same-day microscopy versus standard microscopy for pulmonary tuberculosis: a systematic review and meta-analysisThe Lancet Infectious Diseases, 2013
- Aleatory or epistemic? Does it matter?Structural Safety, 2009