Discovering Hierarchical Neural Archetype Sets. - Gabriele Ciravegna; Pietro Barbiero; Giansalvo Cirrincione; Giovanni Squillero; Alberto Tonda (IIH-MSP (1), 2017)

Nonstationary topological learning with bridges and convex polytopes: the G-EXIN neural network. - Vincenzo Randazzo; Giansalvo Cirrincione; Gabriele Ciravegna; Eros Pasero (IJCNN, 2018)

Unsupervised Gene Identification in Colorectal Cancer. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Eros Pasero; Elio Piccolo (Quantifying and Processing Biomedical and Behavioral Signals, 2019)

Supervised Gene Identification in Colorectal Cancer. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Eros Pasero; Elio Piccolo (Quantifying and Processing Biomedical and Behavioral Signals, 2019)

A Constraint-Based Approach to Learning and Explanation. - Gabriele Ciravegna; Francesco Giannini; Stefano Melacci; Marco Maggini; Marco Gori (AAAI, 2020)

Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network. - Pietro Barbiero; Marta Lovino; Mattia Siviero; Gabriele Ciravegna; Vincenzo Randazzo; Elisa Ficarra; Giansalvo Cirrincione (ICIC (1), 2020)

Human-Driven FOL Explanations of Deep Learning. - Gabriele Ciravegna; Francesco Giannini; Marco Gori; Marco Maggini; Stefano Melacci (IJCAI, 2020)

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers? - Stefano Melacci; Gabriele Ciravegna; Angelo Sotgiu; Ambra Demontis; Battista Biggio; Marco Gori; Fabio Roli (CoRR, 2020)

Topological Gradient-based Competitive Learning. - Pietro Barbiero; Gabriele Ciravegna; Vincenzo Randazzo; Giansalvo Cirrincione (CoRR, 2020)

Gradient-based Competitive Learning: Theory. - Giansalvo Cirrincione; Pietro Barbiero; Gabriele Ciravegna; Vincenzo Randazzo (CoRR, 2020)

The GH-EXIN neural network for hierarchical clustering. - Giansalvo Cirrincione; Gabriele Ciravegna; Pietro Barbiero; Vincenzo Randazzo; Eros Pasero (Neural Networks, 2020)

DNA Microarray Classification: Evolutionary Optimization of Neural Network Hyper-parameters. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Elio Piccolo (Neural Approaches to Dynamics of Signal Exchanges, 2020)

Understanding Cancer Phenomenon at Gene Expression Level by using a Shallow Neural Network Chain. - Pietro Barbiero; Andrea Bertotti; Gabriele Ciravegna; Giansalvo Cirrincione; Elio Piccolo; Alberto Tonda (Neural Approaches to Dynamics of Signal Exchanges, 2020)

Assessing Discriminating Capability of Geometrical Descriptors for 3D Face Recognition by Using the GH-EXIN Neural Network. - Gabriele Ciravegna; Giansalvo Cirrincione; Federica Marcolin; Pietro Barbiero; Nicole Dagnes; Elio Piccolo (Neural Approaches to Dynamics of Signal Exchanges, 2020)

Topological Gradient-based Competitive Learning. - Pietro Barbiero; Gabriele Ciravegna; Vincenzo Randazzo; Eros Pasero; Giansalvo Cirrincione (IJCNN, 2021)

LENs: a Python library for Logic Explained Networks. - Pietro Barbiero; Gabriele Ciravegna; Dobrik Georgiev; Francesco Giannini (CoRR, 2021)

Knowledge-driven Active Learning. - Gabriele Ciravegna; Frédéric Precioso; Marco Gori (CoRR, 2021)

Entropy-Based Logic Explanations of Neural Networks. - Pietro Barbiero; Gabriele Ciravegna; Francesco Giannini; Pietro Lió; Marco Gori; Stefano Melacci (AAAI, 2022)

Minimizing Cross Intersections in Graph Drawing via Linear Splines. - Rida Ghafoor Hussain; Matteo Tiezzi; Gabriele Ciravegna; Marco Gori (ANNPR, 2022)

Extending Logic Explained Networks to Text Classification. - Rishabh Jain; Gabriele Ciravegna; Pietro Barbiero; Francesco Giannini; Davide Buffelli; Pietro Liò (EMNLP, 2022)

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off. - Mateo Espinosa Zarlenga; Pietro Barbiero; Gabriele Ciravegna; Giuseppe Marra; Francesco Giannini; Michelangelo Diligenti; Zohreh Shams; Frédéric Precioso; Stefano Melacci; Adrian Weller; Pietro Lió; Mateja Jamnik (NeurIPS, 2022)

Encryption-agnostic classifiers of traffic originators and their application to anomaly detection. - Daniele Canavese; Leonardo Regano; Cataldo Basile; Gabriele Ciravegna; Antonio Lioy (Comput. Electr. Eng., 2022)

Encoding Concepts in Graph Neural Networks. - Lucie Charlotte Magister; Pietro Barbiero; Dmitry Kazhdan; Federico Siciliano; Gabriele Ciravegna; Fabrizio Silvestri; Pietro Liò; Mateja Jamnik (CoRR, 2022)

Concept Embedding Models. - Mateo Espinosa Zarlenga; Pietro Barbiero; Gabriele Ciravegna; Giuseppe Marra; Francesco Giannini; Michelangelo Diligenti; Zohreh Shams; Frédéric Precioso; Stefano Melacci; Adrian Weller; Pietro Liò; Mateja Jamnik (CoRR, 2022)

A survey on data integration for multi-omics sample clustering. - Marta Lovino; Vincenzo Randazzo; Gabriele Ciravegna; Pietro Barbiero; Elisa Ficarra; Giansalvo Cirrincione (Neurocomputing, 2022)

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers. - Stefano Melacci; Gabriele Ciravegna; Angelo Sotgiu; Ambra Demontis; Battista Biggio; Marco Gori; Fabio Roli (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

On the Two-fold Role of Logic Constraints in Deep Learning. - Gabriele Ciravegna (2022)

Toward Novel Optimizers: A Moreau-Yosida View of Gradient-Based Learning. - Alessandro Betti; Gabriele Ciravegna; Marco Gori; Stefano Melacci; Kevin Mottin; Frédéric Precioso (AI*IA, 2023)

Interpretable Neural-Symbolic Concept Reasoning. - Pietro Barbiero; Gabriele Ciravegna; Francesco Giannini; Mateo Espinosa Zarlenga; Lucie Charlotte Magister; Alberto Tonda; Pietro Lio; Frédéric Precioso; Mateja Jamnik; Giuseppe Marra (ICML, 2023)

Knowledge-Driven Active Learning. - Gabriele Ciravegna; Frédéric Precioso; Alessandro Betti; Kevin Mottin; Marco Gori (ECML/PKDD (1), 2023)

Shall We Stop Using End-to-End Learning? A Short-Survey of Recent Concept-Based Approaches. - Gabriele Ciravegna (WIRN, 2023)

Concept Distillation in Graph Neural Networks. - Lucie Charlotte Magister; Pietro Barbiero; Dmitry Kazhdan; Federico Siciliano; Gabriele Ciravegna; Fabrizio Silvestri; Mateja Jamnik; Pietro Liò (xAI (3), 2023)

Logic Explained Networks. - Gabriele Ciravegna; Pietro Barbiero; Francesco Giannini; Marco Gori; Pietro Liò; Marco Maggini; Stefano Melacci (Artif. Intell., 2023)

Relational Concept Based Models. - Pietro Barbiero; Francesco Giannini; Gabriele Ciravegna; Michelangelo Diligenti; Giuseppe Marra (CoRR, 2023)

Concept-based Explainable Artificial Intelligence: A Survey. - Eleonora Poeta; Gabriele Ciravegna; Eliana Pastor; Tania Cerquitelli; Elena Baralis (CoRR, 2023)

Learning Logic Explanations by Neural Networks. - Gabriele Ciravegna; Francesco Giannini; Pietro Barbiero; Marco Gori; Pietro Lio; Marco Maggini; Stefano Melacci (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Non-invasive AI-powered Diagnostics: The case of Voice-Disorder Detection. - Gabriele Ciravegna; Alkis Koudounas; Marco Fantini; Tania Cerquitelli; Elena Baralis; Erika Crosetti; Giovanni Succo (EDBT/ICDT Workshops, 2024)

Voice Disorder Analysis: a Transformer-based Approach. - Alkis Koudounas; Gabriele Ciravegna; Marco Fantini; Erika Crosetti; Giovanni Succo; Tania Cerquitelli; Elena Baralis (INTERSPEECH, 2024)

Workshop on Human-Interpretable AI. - Gabriele Ciravegna; Mateo Espinosa Zarlenga; Pietro Barbiero; Francesco Giannini; Zohreh Shams; Damien Garreau; Mateja Jamnik; Tania Cerquitelli (KDD, 2024)

Relational Concept Bottleneck Models. - Pietro Barbiero; Francesco Giannini; Gabriele Ciravegna; Michelangelo Diligenti; Giuseppe Marra (NeurIPS, 2024)

Interpretable Concept-Based Memory Reasoning. - David Debot; Pietro Barbiero; Francesco Giannini; Gabriele Ciravegna; Michelangelo Diligenti; Giuseppe Marra (NeurIPS, 2024)

Self-supervised Interpretable Concept-based Models for Text Classification. - Francesco De Santis; Philippe Bich; Gabriele Ciravegna; Pietro Barbiero; Danilo Giordano; Tania Cerquitelli (CoRR, 2024)

A new perspective on optimizers: leveraging moreau-yosida approximation in gradient-based learning. - Alessandro Betti; Gabriele Ciravegna; Marco Gori; Stefano Melacci; Kevin Mottin; Frédéric Precioso (Intelligenza Artificiale, 2024)

Gradient-Based Competitive Learning: Theory. - Giansalvo Cirrincione; Vincenzo Randazzo; Pietro Barbiero; Gabriele Ciravegna; Eros Pasero (Cogn. Comput., 2024)

Graph Neural Networks for Graph Drawing. - Matteo Tiezzi; Gabriele Ciravegna; Marco Gori (IEEE Trans. Neural Networks Learn. Syst., 2024)

Multimodal Fusion Techniques to Enhance Voice Disorder Diagnoses. - Qingqing Liu; Gabriele Ciravegna; Alkis Koudounas; Tania Cerquitelli; Elena Baralis (EDBT/ICDT Workshops, 2025)

Neural Interpretable Reasoning. - Pietro Barbiero; Giuseppe Marra; Gabriele Ciravegna; David Debot; Francesco De Santis; Michelangelo Diligenti; Mateo Espinosa Zarlenga; Francesco Giannini (CoRR, 2025)

V-CEM: Bridging Performance and Intervenability in Concept-based Models. - Francesco De Santis; Gabriele Ciravegna; Philippe Bich; Danilo Giordano; Tania Cerquitelli (CoRR, 2025)

MVP: Multi-source Voice Pathology detection. - Alkis Koudounas; Moreno La Quatra; Gabriele Ciravegna; Marco Fantini; Erika Crosetti; Giovanni Succo; Tania Cerquitelli; Sabato Marco Siniscalchi; Elena Baralis (CoRR, 2025)

Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models. - Francesco De Santis; Philippe Bich; Gabriele Ciravegna; Pietro Barbiero; Danilo Giordano; Tania Cerquitelli (CoRR, 2025)

A Concept-based approach to Voice Disorder Detection. - Davide Ghia; Gabriele Ciravegna; Alkis Koudounas; Marco Fantini; Erika Crosetti; Giovanni Succo; Tania Cerquitelli (CoRR, 2025)