42 citations · 91 across the 6 of their papers we have counts for
7 papers
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…
Gauge/Gravity Duality: Recovering the Bulk from the Boundary using AdS/CFT
Samuel Bilson
Motivated by the holographic principle, within the context of the AdS/CFT Correspondence in the large t'Hooft limit, we investigate how the geometry of certain highly symmetric bul…
An uncertainty-aware Bayesian framework for machine learning classification models: A case study in land cover classification
Samuel Bilson, Miles McCrory, Anna Pustogvar
Ensuring that predictions of machine learning (ML) classification models are accompanied by uncertainty estimates is one of the main pillars of trustworthy AI. Current research in…
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…
Extracting Spacetimes using the AdS/CFT Conjecture: Part II
Samuel Bilson
Motivated by the holographic principle, within the context of the AdS/CFT Correspondence in the large t'Hooft limit, we investigate how the geometry of certain highly symmetric bul…