5 papers
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 Langevin sampler for quantum tomography
Tameem Adel, Abhishek Agarwal, Stéphane Chrétien +4
Quantum tomography involves obtaining a full classical description of a prepared quantum state from experimental results. We propose a Langevin sampler for quantum tomography, that…
A machine learning approach to automation and uncertainty evaluation for self-validating thermocouples
Samuel Bilson, Andrew Thompson, Declan Tucker +1
Thermocouples are in widespread use in industry, but they are particularly susceptible to calibration drift in harsh environments. Self-validating thermocouples aim to address this…
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…