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Michele Fraccaroli

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Publications

International Journals

[6] Alice Bizzarri, Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi. Integration between constrained optimization and deep networks: a survey. Frontiers in Artificial Intelligence, 7, 2024. [ bib | DOI | http ]
[5] Niccolò Ferrari, Michele Fraccaroli, and Evelina Lamma. Grd-net: Generative-reconstructive-discriminative anomaly detection with region of interest attention module. International Journal of Intelligent Systems, 2023:7773481, Sep 2023. [ bib | DOI | http ]
[4] Michele Fraccaroli, Alice Bizzarri, Paolo Casellati, and Evelina Lamma. Exploiting cnn's visual explanations to drive anomaly detection. Applied Intelligence, 2023. [ bib | DOI | http ]
[3] Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi. Symbolic DNN-Tuner: A Python and ProbLog-based system for optimizing deep neural networks hyperparameters. SoftwareX, 17:100957, 2022. [ bib | DOI | http ]
[2] Arnaud Nguembang Fadja, Michele Fraccaroli, Alice Bizzarri, Giulia Mazzuchelli, and Evelina Lamma. Neural-symbolic ensemble learning for early-stage prediction of critical state of covid-19 patients. Medical & Biological Engineering & Computing, 2022. [ bib | DOI | http ]
[1] Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi. Symbolic DNN-Tuner. Machine Learning, © Springer, 2021. [ bib | DOI ]

Book Chapters

[1] Riccardo Zese, Elena Bellodi, Michele Fraccaroli, Fabrizio Riguzzi, and Evelina Lamma. Neural networks and deep learning fundamentals. In Rino Micheloni and Cristian Zambelli, editors, Machine Learning and Non-volatile Memories, pages 23--42. Springer International Publishing, Cham, 2022. [ bib | DOI | http ]

International Conferences

[3] Michele Fraccaroli, Fabrizio Riguzzi, and Evelina Lamma. Exploiting parameters learning for hyper-parameters optimization in deep neural networks. In Yuliya Lierler, Jose F. Morales, Carmine Dodaro, Veronica Dahl, Martin Gebser, and Tuncay Tekle, editors, Proceedings of the 38th International Conference on Logic Programming (Technical Communications), Recently Published Research track, volume 364 of Electronic Proceedings in Theoretical Computer Science, pages 142--144, Waterloo, Australia, 2022. Open Publishing Association. [ bib | DOI | http ]
[2] Michele Fraccaroli, Giulia Mazzucchelli, and Alice Bizzarri. Machine learning techniques for extracting relevant features from clinical data for COVID-19 mortality prediction. In 2021 Symposium on Computers and Communications (ISCC): 26th IEEE Symposium on Computers and Communications - Workshop on ICT Solutions for eHealth (ICTS4eHealth) (ICTS4eHealth2021), pages 1--7, Athens, Greece, September 2021. [ bib | DOI ]
[1] Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi. Automatic setting of DNN hyper-parameters by mixing Bayesian Optimization and tuning rules. In Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Giorgio Jansen, Vincenzo Sciacca, Panos Pardalos, Giovanni Giuffrida, and Renato Umeton, editors, Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I, volume 12565 of Lecture Notes in Computer Science, pages 477--488, Cham, 2020. © Springer, Springer International Publishing. The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-030-64583-0_43. [ bib | DOI | .pdf ]

National Workshops

[1] Michele Fraccaroli, Alice Bizzarri, Paolo Casellati, and Evelina Lamma. Cross entropy overlap distance. Accepted and Presented at ITAL-IA 2022, workshop on AI for Industry, feb 2022. [ bib | .pdf ]

 

Awards

  • 2021: Premio Nazionale Ricerca Big Data & Artificial Intelligence WMF-IFAB