6 citations · 18 across the 5 of their papers we have counts for
5 papers
Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics
Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +4
Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning. While initial NeSy approaches have targeted mainly symbolic…
T-ILR: a Neurosymbolic Integration for LTLf
Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +3
State-of-the-art approaches for integrating symbolic knowledge with deep learning architectures have demonstrated promising results in static domains. However, methods to handle te…
Generating Counterfactual Explanations Under Temporal Constraints
Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini +2
Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading t…
Generating the Traces You Need: A Conditional Generative Model for Process Mining Data
Riccardo Graziosi, Massimiliano Ronzani, Andrei Buliga +5
In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing th…
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini +2
Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field…