2 citations · 2 across the 7 of their papers we have counts for
9 papers
A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification
Francesco Spinnato
Irregular time series, characterized by non-uniform sampling intervals, missing observations, and variable lengths, are ubiquitous in healthcare, mobility, and environmental monito…
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
Miquel Miró-Nicolau, Francesco Spinnato, Riccardo Guidotti
Evaluating explainable Artificial Intelligence (XAI) methods is a challenging task due to the lack of reliable evaluation procedures and, in particular, the absence of ground truth…
An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks
Khaleda Papry, Francesco Spinnato, Marco Fiore +2
As 5G networks continue to evolve to deliver high speed, low latency, and reliable communications, ensuring uninterrupted service has become increasingly critical. While millimeter…
Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms
Riccardo Guidotti, Martina Cinquini, Marta Marchiori Manerba +2
Interpretable-by-design models are crucial for fostering trust, accountability, and safe adoption of automated decision-making models in real-world applications. In this paper we f…
An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items
Luca Corbucci, Javier Alejandro Borges Legrottaglie, Francesco Spinnato +2
Accurately identifying items forgotten during a supermarket visit and providing clear, interpretable explanations for recommending them remains an underexplored problem within the…
MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
Dawid Płudowski, Francesco Spinnato, Piotr Wilczyński +4
Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome. However, applying counterfactual met…
Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields
Francesco Spinnato, Riccardo Guidotti, Anna Monreale +1
The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models in ensemble hybrids, representing time…
A Protocol for Continual Explanation of SHAP
Andrea Cossu, Francesco Spinnato, Riccardo Guidotti +1
Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge. Given the dynamic nature of such environments,…
Modeling Events and Interactions through Temporal Processes -- A Survey
Angelica Liguori, Luciano Caroprese, Marco Minici +5
In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these seq…