4 papers
Fast, close, non-singular and property-preserving approximations of entropic measures
Illia Horenko, Davide Bassetti, Lukáš PospÃÅ¡il
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…
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 PospÃÅ¡il, 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…
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…