Comparing the quality of neural network uncertainty estimates for classification problems-Reference-Cited by-同舟云学术

Comparing the quality of neural network uncertainty estimates for classification problems

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
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Author:

Ries Daniel¹,Michalenko Joshua²,Ganter Tyler³,Baiyasi Rashad Imad-Fayez⁴,Adams Jason⁵

Affiliation:

1. Sandia National Laboratories,Statistics and Data Analytics,Albuquerque,USA

2. Sandia National Laboratories,Proliferation Signature and Data Exploitation,Albuquerque,USA

3. Sandia National Laboratories,Applied Machine Intelligence,Albuquerque,USA

4. Sandia National Laboratories,Mission Algorithms R&S,Albuquerque,USA

5. Sandia National Laboratories,Statistical Sciences,Albuquerque,USA

Funder

U.S. Department of Energy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10068862/10068777/10069951.pdf?arnumber=10069951

Reference32 articles.

1. Second opinion needed: communicating uncertainty in medical machine learning

2. Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

3. Deep learning

4. Simple and scalable predictive uncertainty estimation using deep ensembles;lakshminarayanan;Conference on Neural Information Processing Systems,2017

5. Quality of uncertainty quantification for bayesian neural network inference;yao;Proceedings of the International Conference on Machine Learning,2019

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