5 papers
Distillation and Interpretability of Ensemble Forecasts of ENSO Phase using Entropic Learning
Michael Groom, Davide Bassetti, Illia Horenko +1
This paper introduces a distillation framework for an ensemble of entropy-optimal Sparse Probabilistic Approximation (eSPA) models, trained exclusively on satellite-era observation…
An entropy-optimal path to humble AI
Davide Bassetti, Lukáš Pospíšil, Michael Groom +2
Progress of AI has led to very successful, but by no means humble models and tools, especially regarding (i) the huge and further exploding costs and resources they demand, and (ii…
Fast, close, non-singular and property-preserving approximations of entropic measures
Illia Horenko, Davide Bassetti, Lukáš Pospíšil
Entropic measures like Shannon entropy (SE), its quantum mechanical analogue von Neumann entropy, and Kullback-Leibler divergence (KL) are key components in many tools used in phys…
Entropic learning enables skilful forecasts of ENSO phase at up to two years lead time
Michael Groom, Davide Bassetti, Illia Horenko +1
This paper extends previous work (Groom et al., \emph{Artif. Intell. Earth Syst.}, 2024) in applying the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm to pred…
Gauge-optimal approximate learning for small data classification problems
Edoardo Vecchi, Davide Bassetti, Fabio Graziato +2
Small data learning problems are characterized by a significant discrepancy between the limited amount of response variable observations and the large feature space dimension. In t…