Expressing uncertainty in neural networks for production systems

Abstract

The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network's ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.