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stat.ML2025
Mathematical Foundation of Interpretable Equivariant Surrogate Models
Jacopo Joy Colombini, Filippo Bonchi, Francesco Giannini +3
This paper introduces a rigorous mathematical framework for neural network explainability, and more broadly for the explainability of equivariant operators called Group Equivariant…
stat.ML2023
A topological model for partial equivariance in deep learning and data analysis
Lucia Ferrari, Patrizio Frosini, Nicola Quercioli +1
In this article, we propose a topological model to encode partial equivariance in neural networks. To this end, we introduce a class of operators, called P-GENEOs, that change data…