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[8]
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Alice Bizzarri, Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi.
Integration between constrained optimization and deep networks: a
survey.
Frontiers in Artificial Intelligence, 7, 2024.
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[7]
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Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli, Alice Bizzarri, and
Evelina Lamma.
Integration of deep generative anomaly detection algorithm in
high-speed industrial line.
2024.
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[6]
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Nicola Fullin, Michele Fraccaroli, Mirko Francioni, Stefano Fabbri, Angelo
Ballaera, Paolo Ciavola, and Monica Ghirotti.
Detection of cliff top erosion drivers through machine learning
algorithms between portonovo and trave cliffs (ancona, italy).
Remote Sensing, 16(14), 2024.
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[5]
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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.
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[4]
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Michele Fraccaroli, Alice Bizzarri, Paolo Casellati, and Evelina Lamma.
Exploiting cnn's visual explanations to drive anomaly detection.
Applied Intelligence, 2023.
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[3]
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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.
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[2]
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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.
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[1]
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Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi.
Symbolic DNN-Tuner.
Machine Learning, © Springer, 2021.
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[5]
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Elena Bellodi, Davide Bertozzi, Alice Bizzarri, Michele Favalli, Michele
Fraccaroli, and Riccardo Zese.
Efficient resource-aware neural architecture search with a
neuro-symbolic approach.
pages 171--178, 2023.
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[4]
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Damiano Azzolini, Alice Bizzarri, Michele Fraccaroli, Francesco Bertasi, and
Evelina Lamma.
A machine learning pipeline to analyse multispectral and
hyperspectral images: Full/regular research paper (csci-rthi).
In 2023 International Conference on Computational Science and
Computational Intelligence (CSCI), pages 1306--1311, 2023.
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[3]
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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.
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[2]
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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.
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[1]
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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.
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