# Authors Title Details Date Pdf/Links/Bibtex Keywords
7 Nakanishi T. Longo L.Approximate-Inverse Explainability of beta–VAE Latents for Multichannel EEG Participant-generalised Topographical Representation Learning IEEE Access 2025 Electroencephalography • Brain modeling • Spatial coherence • Scalp • Perturbation methods • Computational modeling • Explainable AI • Deep learning • Electroencephalography • VAE • topographic mapping • approximate inverse model explanations • generative deep learning • representation learning
10.1109/ACCESS.2025.3635543
6Ephrem Tibebe Mekonnen; Longo L., Dondio P.LOMATCE: LOcal Model-Agnostic Time-series Classification Explanations IEEE Access 2025 Time series analysis • Adaptation models • Explainable AI • Predictive models • Data models • Closed box • Perturbation methods • Computational modeling • Deep learning • Kernel • Explainable Artificial Intelligence • Model-agnostic • Time series • Post hoc • Deep learning • XAI
10.1109/ACCESS.2025.3625442
5Vilone G., Longo L. Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees eXplainable Artificial Intelligence, The World Conference (xAI-2025) 2025 Logical Analysis • Graph Theory • Graph Theory in Probability • Machine Learning • Reasoning • Symbolic AI • Explainable AI • Surrogate models • Computational Argumentation • Rule-based systems • Decision-trees • Dense Neural Networks • Deep learning
10.1007/978-3-032-08333-3_5
4Ahmed T., Biecek P. Longo L. Latent Space Interpretation and Mechanistic Clipping of Subject-Specific Variational Autoencoders of EEG Topographic Maps for Artefacts Reduction eXplainable Artificial Intelligence, The World Conference (xAI-2025) 2025 Electroencephalography • Spectral topographic maps • Subject-specific • Variational autoencoder • Latent space • interpretability • Artefacts removal • Deep learning • full automation • explainable AI
10.1007/978-3-032-08327-2_16
3Gupta G., Qureshi M.A., Longo L.A Global Post Hoc XAI Method For Interpreting LSTM Using Deterministic Finite State Automata The Irish conference on Artificial Intelligence and Cognitive Science 2025 RNN • interpretability • Explainable AI • LSTM • Deterministic Finite State Automata • k-means clustering • Recurrent Neural Networks
2Rizzo L., Verda D., Berretta S., Longo L. A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AI Machine Learning and Knowledge Extraction 2024 rule-base AI • explainable artificial intelligence • computational argumentation • defeasible reasoning • Artificial Intelligence
10.3390/make6030101
1Vilone G. Longo L.A global model-agnostic XAI method for the automatic formation of an abstract argumentation framework and its objective evaluation. 1st Int. Workshop on Argumentation for eXplainable AI (with 9th Int. Conference on Computational Models of Argument, COMMA 2022) 2022 Explainable artificial intelligence • Argumentation • Non-monotonic reasoning • Method evaluation • Metrics of explainability
# Authors Title Details Date Pdf/Links/Bibtex Keywords