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[10]
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Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Marco Alberti, and Evelina
Lamma.
Probabilistic inductive constraint logic.
Machine Learning, pages 1--32, 2020.
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[9]
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Fabrizio Riguzzi Michele Fraccaroli, Evelina Lamma.
Automatic setting of DNN hyper-parameters by mixing Bayesian
Optimization and tuning rules.
In The Sixth International Conference on Machine Learning,
Optimization, and Data Science 2020. To be published., Lecture Notes in
Computer Science. Springer, 2020.
[ bib ]
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[8]
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Damiano Azzolini, Fabrizio Riguzzi, and Evelina Lamma.
An analysis of Gibbs sampling for probabilistic logic programs.
In Carmine Dodaro, George Aristidis Elder, Wolfgang Faber, Jorge
Fandinno, Martin Gebser, Markus Hecher, Emily LeBlanc, Michael Morak, and
Jessica Zangari, editors, Workshop on Probabilistic Logic Programming
(PLP 2020), volume 2678 of CEUR Workshop Proceedings, pages 1--13,
Aachen, Germany, 2020. © by the authors, Sun SITE Central
Europe.
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[7]
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Damiano Azzolini, Elena Bellodi, Alessandro Brancaleoni, Fabrizio Riguzzi, and
Evelina Lamma.
Modeling bitcoin lightning network by logic programming.
In Francesco Ricca, Alessandra Russo, Sergio Greco, Nicola Leone,
Alexander Artikis, Gerhard Friedrich, Paul Fodor, Angelika Kimmig, Francesca
Lisi, Marco Maratea, Alessandra Mileo, and Fabrizio Riguzzi, editors,
Proceedings of the 36th International Conference on Logic Programming
(Technical Communications), pages 258--260, Waterloo, Australia, 2020.
© by the authors, Open Publishing Association.
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DOI |
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http ]
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[6]
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Damiano Azzolini, Fabrizio Riguzzi, and Evelina Lamma.
Modeling smart contracts with probabilistic logic programming.
In Witold Abramowicz and Gary Klein, editors, Business
Information Systems Workshops, volume 394 of Lecture Notes in Business
Information Processing, pages 86--98, Cham, 2020. © Springer,
Springer International Publishing.
The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-030-61146-0_7.
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[5]
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Damiano Azzolini, Fabrizio Riguzzi, and Evelina Lamma.
Analyzing transaction fees with probabilistic logic programming.
In Witold Abramowicz and Rafael Corchuelo, editors, Business
Information Systems Workshops BIS 2019, volume 373 of Lecture Notes in
Business Information Processing, pages 243--254, Cham, 2019. © Springer, Springer International Publishing.
The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-030-36691-9_21.
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[4]
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Damiano Azzolini, Fabrizio Riguzzi, and Evelina Lamma.
Studying transaction fees in the bitcoin blockchain with
probabilistic logic programming.
Information, 10(11):335, © CCBY, 2019.
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DOI |
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[3]
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Damiano Azzolini, Fabrizio Riguzzi, Evelina Lamma, and Franco Masotti.
A comparison of MCMC sampling for probabilistic logic programming.
In Mario Alviano, Gianluigi Greco, and Francesco Scarcello, editors,
Proceedings of the 18th Conference of the Italian Association for
Artificial Intelligence (AI*IA2019), Rende, Italy 19-22 November 2019,
volume 11946 of Lecture Notes in Computer Science, pages 18--29,
Heidelberg, Germany, 2019. © Springer, Springer.
The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-030-35166-3_2.
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DOI |
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[2]
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Giuseppe Cota, Fabrizio Riguzzi, Evelina Lamma, and Riccardo Zese.
KRaider: a crawler for linked data.
In Alberto Casagrande and Eugenio Omodeo, editors, Proceedings
of the 34th Italian Conference on Computational Logic, volume 2396 of
CEUR Workshop Proceedings, pages 202--216, Aachen, Germany, 2019.
© by the authors, Sun SITE Central Europe.
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[1]
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Riccardo Zese, Giuseppe Cota, Evelina Lamma, Elena Bellodi, and Fabrizio
Riguzzi.
Probabilistic DL reasoning with pinpointing formulas: A
prolog-based approach.
Theory and Practice of Logic Programming, 19(3):449--476,
© Cambridge University Press, 2019.
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