12 citations · 17 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction
Illia Horenko, Lukas Pospisil
In many data science applications, the objective is to extract appropriately-ordered smooth low-dimensional data patterns from high-dimensional data sets. This is challenging since…
On a scalable entropic breaching of the overfitting barrier in machine learning
Illia Horenko
Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size is not enough to pr…