7 citations · 12 across the 7 of their papers we have counts for
8 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…
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…
Matrix rigidity and the ill-posedness of Robust PCA and matrix completion
Jared Tanner, Andrew Thompson, Simon Vary
Robust Principal Component Analysis (PCA) (Candes et al., 2011) and low-rank matrix completion (Recht et al., 2010) are extensions of PCA to allow for outliers and missing entries…