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F. Spinnato

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#artificial intelligence Preprint Sep 2026

A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification

Irregular time series, characterized by non-uniform sampling intervals, missing observations, and variable lengths, are ubiquitous in healthcare, mobility, and environmental monitoring, yet effective and interpretable classifiers for this setting are limited. Existing approaches often rely on imputation, which can obsc...

F. Spinnato · 0 citations
#artificial intelligence Preprint Sep 2026

A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods

A framework for the evaluation of XAI methods based on synthetic ground truth that relies on controlled interventions to generate synthetic datasets in which the importance of input components can be determined by design, allowing a comprehensive assessment of explanation methods in heterogeneous settings.

Miquel Miró-Nicolau, F. Spinnato, Riccardo Guidotti · 0 citations

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