5 papers · 1 filter
Extending confidence calibration to generalised measures of variation
Andrew Thompson, Vivek Desai
We propose the Variation Calibration Error (VCE) metric for assessing the calibration of machine learning classifiers. The metric can be viewed as an extension of the well-known Ex…
Uncertainty propagation through trained multi-layer perceptrons: Exact analytical results
Andrew Thompson, Miles McCrory
We give analytical results for propagation of uncertainty through trained multi-layer perceptrons (MLPs) with a single hidden layer and ReLU activation functions. More precisely, w…
A metrological framework for uncertainty evaluation in machine learning classification models
Samuel Bilson, Maurice Cox, Anna Pustogvar +1
Machine learning (ML) classification models are increasingly being used in a wide range of applications where it is important that predictions are accompanied by uncertainties, inc…
Trustworthy Artificial Intelligence in the Context of Metrology
Tameem Adel, Sam Bilson, Mark Levene +1
We review research at the National Physical Laboratory (NPL) in the area of trustworthy artificial intelligence (TAI), and more specifically trustworthy machine learning (TML), in…
Analytical results for uncertainty propagation through trained machine learning regression models
Andrew Thompson
Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by princ…