Matteo RIZZO

Qualifica
Dottorando
Dottorato
INFORMATICA
38° Ciclo - Immatricolati nel 2022
Area tematica
PNRR - Deep learning for explainable food recognition, characterization and quality assessment
Supervisore
Albarelli Andrea / Lucchese Claudio
E-mail
matteo.rizzo@unive.it
956738@stud.unive.it
Sito web
www.unive.it/persone/matteo.rizzo (scheda personale)
Struttura
Dipartimento di Scienze Ambientali, Informatica e Statistica
Sito web struttura: https://www.unive.it/dais

Frasson, Giada; Rizzo, Matteo; Nobile, Marco Salvatore; Lupi, Amalia; Quaia, Emilio Assessing the Value of Explainable Artificial Intelligence for Magnetic Resonance Imaging , Explainable Artificial Intelligence. xAI 2025., Springer, pp. 423-447, Convegno: World Conference on Explainable Artificial Intelligence (ISBN 9783032083166; 9783032083173) (ISSN 1865-0929)
DOI 2025, Articolo in Atti di convegno - Scheda ARCA: 10278/5105029


Zangari, Alessandro; Marcuzzo, Matteo; Rizzo, Matteo; Giudice, Lorenzo; Albarelli, Andrea; Gasparetto, Andrea Hierarchical Text Classification and Its Foundations: A Review of Current Research in ELECTRONICS, vol. 13 (ISSN 2079-9292)
DOI - URL correlato 2024, Articolo su rivista - Scheda ARCA: 10278/5055640


Alessandro Zangari; Matteo Marcuzzo; Matteo Rizzo; Andrea Albarelli; Andrea Gasparetto Crossing the Divide: Designing Layers of Explainability , Lecture Notes in Artificial Intelligence (LNAI, volume 15164), Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada, pp. 253-265, Convegno: International Conference on Artificial Intelligence and Soft Computing, 16/06/2024-20/06/2024 (ISBN 9783031843525; 9783031843532) (ISSN 0302-9743)
DOI 2024, Articolo in Atti di convegno - Scheda ARCA: 10278/5093691


Albarelli A.; Lucchese C.; Rizzo M.; Veneri A. On the Application of a Common Theoretical Explainability Framework in Information Retrieval , CEUR Workshop Proceedings, CEUR-WS, vol. 3802, pp. 43-52, Convegno: 14th Italian Information Retrieval Workshop, IIR 2024, 2024 (ISSN 1613-0073)
2024, Articolo in Atti di convegno - Scheda ARCA: 10278/5084247


Rizzo M.; Marcuzzo M.; Zangari A.; Schiavinato M.; Albarelli A.; Gasparetto A. Stop Overkilling Simple Tasks with Black-Box Models, Use More Transparent Models Instead , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH, vol. 14892, pp. 279-293, Convegno: 4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024, 2024 (ISBN 9789819787012; 9789819787029) (ISSN 0302-9743)
DOI 2024, Articolo in Atti di convegno - Scheda ARCA: 10278/5093692