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Dominici, G., Barbiero, P., Giannini, F., Gjoreski, M., Marra, G., & Langheinrich, M. (2024). Climbing the Ladder of Interpretability with Counterfactual Concept Bottleneck Models. arXiv preprint arXiv:2402.01408.
Keskin, O., Lupidi, A. M., Fioravanti, S., Magister, L. C., Barbiero, P., Lio, P., & Giannini, F. (2023, September). Bridging Equational Properties and Patterns on Graphs: an AI-Based Approach. In Topological, Algebraic and Geometric Learning Workshops 2023 (pp. 156-168). PMLR.
Barbiero, P., Giannini, F., Ciravegna, G., Diligenti, M., & Marra, G. (2023). Relational Concept Based Models. arXiv preprint arXiv:2308.11991.
Fioravanti, S., Zugarini, A., Giannini, F., Rigutini, L., Maggini, M., & Diligenti, M. (2023, June). Linguistic Feature Injection for Efficient Natural Language Processing. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 01-07). IEEE.
Barbiero, P., Fioravanti, S., Giannini, F., Tonda, A., Lio, P., & Di Lavore, E. (2023). Categorical foundations of explainable ai: A unifying formalism of structures and semantics. arXiv preprint arXiv:2304.14094.
Barbiero, P., Ciravegna, G., Giannini, F., Zarlenga, M. E., Magister, L. C., Tonda, A., ... & Marra, G. (2023, July). Interpretable neural-symbolic concept reasoning. In International Conference on Machine Learning (pp. 1801-1825). PMLR.
Diligenti, M., Giannini, F., Fioravanti, S., Graziani, C., Falaschi, M., & Marra, G. (2023, June). Enhancing Embedding Representations of Biomedical Data using Logic Knowledge. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Giannini, F., Diligenti, M., Maggini, M., Gori, M., & Marra, G. (2023). T-norms driven loss functions for machine learning. Applied Intelligence, 53(15), 18775-18789.
Ciravegna, G., Giannini, F., Barbiero, P., Gori, M., Lio, P., Maggini, M., & Melacci, S. (2023). Learning Logic Explanations by Neural Networks. In Compendium of Neurosymbolic Artificial Intelligence (pp. 547-558). IOS Press.
Giannini, F., Fioravanti, S., Keskin, O., Lupidi, A., Magister, L. C., Lió, P., & Barbiero, P. (2024). Interpretable Graph Networks Formulate Universal Algebra Conjectures. Advances in Neural Information Processing Systems, 36.
Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lió, P., Maggini, M., & Melacci, S. (2023). Logic explained networks. Artificial Intelligence, 314, 103822.
Espinosa Zarlenga, M., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., ... & Jamnik, M. (2022). Concept embedding models: Beyond the accuracy-explainability trade-off. Advances in Neural Information Processing Systems, 35, 21400-21413.
Jain, R., Ciravegna, G., Barbiero, P., Giannini, F., Buffelli, D., & Lio, P. (2022, December). Extending Logic Explained Networks to Text Classification. In Empirical Methods in Natural Language Processing.
Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M., & Melacci, S. (2022, June). Entropy-based logic explanations of neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 6, pp. 6046-6054).
Diligenti, M., Giannini, F., Gori, M., Maggini, M., Marra, G. (2022). A constraint-based approach to learning and reasoning. Neuro-symbolic artificial intelligence: the state of the art, 342, 192.
Marra, G., Diligenti, M., & Giannini, F. (2021). Relational reasoning networks. arXiv preprint arXiv:2106.00393.
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Ciravegna, G., Giannini, F., Melacci, S., Maggini, M., & Gori, M. (2020, April). A constraint-based approach to learning and explanation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 3658-3665).
Marra, G., Diligenti, M., Giannini, F., Gori, M., & Maggini, M. (2020). Relational Neural Machines. In ECAI 2020 (pp. 1340-1347). IOS Press.
Marra, G., Diligenti, M., Faggi, L., Giannini, F., Gori, M., & Maggini, M. (2020). Inference in relational neural machines. In CEUR WORKSHOP PROCEEDINGS (Vol. 2659, pp. 71-74). CEUR-WS.
Giannini, F., Marra, G., Diligenti, M., Maggini, M., & Gori, M. (2020). On the relation between loss functions and t-norms. In Inductive Logic Programming: 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3–5, 2019, Proceedings 29 (pp. 36-45). Springer International Publishing.
Marra, G., Giannini, F., Diligenti, M., & Gori, M. (2019, September). Lyrics: A general interface layer to integrate logic inference and deep learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 283-298). Cham: Springer International Publishing.
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Giannini, F., & Maggini, M. (2019). Conditions for Unnecessary Logical Constraints in Kernel Machines. In Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II 28 (pp. 608-620). Springer International Publishing.
Marra, G., Giannini, F., Diligenti, M., & Gori, M. (2019). Constraint-based visual generation. In Artificial Neural Networks and Machine Learning–ICANN 2019: Image Processing: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III 28 (pp. 565-577). Springer International Publishing.
Giannini, F., Diligenti, M., Gori, M., & Maggini, M. (2018). On a convex logic fragment for learning and reasoning. IEEE Transactions on Fuzzy Systems, 27(7), 1407-1416.
Giannini, F., Diligenti, M., Gori, M., & Maggini, M. (2018, April). Characterization of the convex łukasiewicz fragment for learning from constraints. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
Giannini, F., Laveglia, V., Rossi, A., Zanca, D., & Zugarini, A. (2017). Neural networks for beginners. A fast implementation in matlab, torch, tensorflow. arXiv preprint arXiv:1703.05298.
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