no code implementations • 25 Aug 2023 • Lucia Ferrari, Patrizio Frosini, Nicola Quercioli, Francesca Tombari
In this article, we propose a topological model to encode partial equivariance in neural networks.
no code implementations • 3 Mar 2021 • Pasquale Cascarano, Patrizio Frosini, Nicola Quercioli, Amir Saki
Group equivariant non-expansive operators have been recently proposed as basic components in topological data analysis and deep learning.
no code implementations • 7 Aug 2020 • Giovanni Bocchi, Stefano Botteghi, Martina Brasini, Patrizio Frosini, Nicola Quercioli
This result makes available a new method to build linear $G$-equivariant operators in the finite setting.
1 code implementation • 31 Dec 2018 • Mattia G. Bergomi, Patrizio Frosini, Daniela Giorgi, Nicola Quercioli
The aim of this paper is to provide a general mathematical framework for group equivariance in the machine learning context.