all AI news
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?. (arXiv:2209.03302v1 [cs.LG])
Sept. 8, 2022, 1:11 a.m. | Eyke Hüllermeier
cs.LG updates on arXiv.org arxiv.org
This short note is a critical discussion of the quantification of aleatoric
and epistemic uncertainty in terms of conditional entropy and mutual
information, respectively, which has recently been proposed in machine learning
and has become quite common since then. More generally, we question the idea of
an additive decomposition of total uncertainty into its aleatoric and epistemic
constituents.
arxiv entropy information machine machine learning uncertainty
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Data Engineer
@ Publicis Groupe | New York City, United States
Associate Principal Robotics Engineer - Research.
@ Dyson | United Kingdom - Hullavington Office
Duales Studium mit vertiefter Praxis: Bachelor of Science Künstliche Intelligenz und Data Science (m/w/d)
@ Gerresheimer | Wackersdorf, Germany
AI/ML Engineer (TS/SCI) {S}
@ ARKA Group, LP | Aurora, Colorado, United States
Data Integration Engineer
@ Find.co | Sliema
Data Engineer
@ Q2 | Bengaluru, India