arXiv:1811.11669 Abstract | arXiv Analytics

arXiv:1811.11669 [cs.LG]Abstract References Reviews Resources

Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview

Published 2018-11-28Version 1

Quantifying and managing uncertainties that occur when data-driven models such as those provided by AI and machine learning methods are applied is crucial. This whitepaper provides a brief motivation and first overview of the state of the art in identifying and quantifying sources of uncertainty for data-driven components as well as means for analyzing their impact.

Categories: cs.LG, stat.ML

Subjects: 68T01

Keywords: machine learning models, managing sources, uncertainty, identifying, data-driven components

Related articles: Most relevant | Search more

arXiv:1908.02781 [cs.LG] (Published 2019-08-07)

Flood Prediction Using Machine Learning Models: Literature Review

Amir Mosavi, Pinar Ozturk, Kwok-wing Chau

arXiv:2001.11757 [cs.LG] (Published 2020-01-31)

Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

Giorgio Visani, Enrico Bagli, Federico Chesani, Alessandro Poluzzi, Davide Capuzzo

arXiv:1911.03030 [cs.LG] (Published 2019-11-08)

Certified Data Removal from Machine Learning Models