From the 2 of 7 linked papers with an AI index.
7 papers
On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems
Illia Horenko
The paper introduces Entropy-Optimal Manifold Regression (EOMR), a method that simultaneously selects relevant feature subsets and subspaces for nonlinear, nonstationary regression…
Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics
Kübra Kalkan, Atreya Majumdar, Ross Knapman +5
The paper presents a method that uses statistical descriptors of thermally driven magnetization dynamics, especially latent entropy, to infer local magnetic material parameters suc…
Linearly-scalable and entropy-optimal learning of nonstationary and nonlinear manifolds
Illia Horenko
We propose an Entropy-Optimal Manifold Clustering (EOMC) - and show that it mitigates the cost scaling and robustness issues of the existing dimensionality reduction and manifold l…
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…