Uncertainty quantification in neural network classifiers -- a local linear approach

DOI10.1016/J.AUTOMATICA.2024.111563WikidataQ128608750 ScholiaQ128608750MaRDI QIDQ6546838

Fredrik Gustafsson, Magnus Malmström, Daniel Axehill, Isaac Skog

Publication date: 30 May 2024

Published in: Automatica (Search for Journal in Brave)

zbMATH Keywords

neural networksidentification and model reductionuncertainty descriptionsinformation and sensor fusion

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Robust distributed maximum likelihood estimation with dependent quantized data
• Bayesian frequentist bounds for machine learning and system identification
• Prediction Intervals for Artificial Neural Networks
• Machine Learning
• Strictly Proper Scoring Rules, Prediction, and Estimation
• On the sensitivity of pose estimation neural networks: rotation parameterizations, Lipschitz constants, and provable bounds

This page was built for publication: Uncertainty quantification in neural network classifiers -- a local linear approach

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6546838)