Semantic Interpretation of Deep Neural Networks Based on Continuous Logic. - József Dombi 001; Orsolya Csiszár; Gábor Csiszár (CoRR, 2019)

Natural Invariance Explains Empirical Success of Specific Membership Functions, Hedge Operations, and Negation Operations. - Julio C. Urenda; Orsolya Csiszár; Gábor Csiszár; József Dombi 001; György Eigner; Vladik Kreinovich (NAFIPS, 2020)

Why Squashing Functions in Multi-Layer Neural Networks. - Julio C. Urenda; Orsolya Csiszár; Gábor Csiszár; József Dombi 001; Olga Kosheleva; Vladik Kreinovich; György Eigner (SMC, 2020)

Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic. - Daniel Zeltner; Benedikt Schmid; Gábor Csiszár; Orsolya Csiszár (CoRR, 2020)

Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. - Orsolya Csiszár; Gábor Csiszár; József Dombi 001 (Knowl. Based Syst., 2020)

How to implement MCDM tools and continuous logic into neural computation?: Towards better interpretability of neural networks. - Orsolya Csiszár; Gábor Csiszár; József Dombi 001 (Knowl. Based Syst., 2020)

Why Kappa Regression? - Julio C. Urenda; Orsolya Csiszár; Gábor Csiszár; József Dombi 001; György Eigner; Olga Kosheleva; Vladik Kreinovich (IFSA/EUSFLAT/AGOP, 2021)

Squashing activation functions in benchmark tests: Towards a more eXplainable Artificial Intelligence using continuous-valued logic. - Daniel Zeltner; Benedikt Schmid; Gábor Csiszár; Orsolya Csiszár (Knowl. Based Syst., 2021)

Parametric activation functions modelling fuzzy connectives for better explainability of neural models. - Luca Sára Pusztaházi; Gábor Csiszár; Michael S. Gashler; Orsolya Csiszár (SISY, 2022)

Why Fuzzy Control Is Often More Robust (and Smoother): A Theoretical Explanation. - Orsolya Csiszár; Gábor Csiszár; Olga Kosheleva; Martine Ceberio; Vladik Kreinovich (SSCI, 2023)

Uninorm-like parametric activation functions for human-understandable neural models. - Orsolya Csiszár; Luca Sára Pusztaházi; Lehel Dénes-Fazakas; Michael S. Gashler; Vladik Kreinovich; Gábor Csiszár (Knowl. Based Syst., 2023)