11 citations · 55 across the 33 of their papers we have counts for
5 papers · 1 filter
Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators
Lorenzo Mario Amorosa, Francesco Conti, Nicola Quercioli +4
As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and…
GENEOnet: Statistical analysis supporting explainability and trustworthiness
Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti +5
Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings su…
A novel approach to graph distinction through GENEOs and permutants
Giovanni Bocchi, Massimo Ferri, Patrizio Frosini
The theory of Group Equivariant Non-Expansive Operators (GENEOs) was initially developed in Topological Data Analysis for the geometric approximation of data observers, including t…
Parametric machines: a fresh approach to architecture search
Pietro Vertechi, Mattia G. Bergomi
Using tools from topology and functional analysis, we provide a framework where artificial neural networks, and their architectures, can be formally described. We define the notion…
Towards a topological-geometrical theory of group equivariant non-expansive operators for data analysis and machine learning
Mattia G. Bergomi, Patrizio Frosini, Daniela Giorgi +1
The aim of this paper is to provide a general mathematical framework for group equivariance in the machine learning context. The framework builds on a synergy between persistent ho…