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ORCIDOrsolya Csiszár
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2020 – today
- 2024
[j11]Luca Sára Pusztaházi
, György Eigner, Orsolya Csiszár:
Parametric Activation Functions for Neural Networks: A Tutorial Survey. IEEE Access 12: 168626-168644 (2024)- 2023
- [j10]Juan G. Díaz Ochoa, Lukas Maier, Orsolya Csiszár:
Bayesian logical neural networks for human-centered applications in medicine. Frontiers Bioinform. 3 (2023) - [j9]Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár:
Uninorm-like parametric activation functions for human-understandable neural models. Knowl. Based Syst. 260: 110095 (2023) - [c6]Orsolya Csiszár, Gábor Csiszár, Olga Kosheleva, Martine Ceberio, Vladik Kreinovich:
Why Fuzzy Control Is Often More Robust (and Smoother): A Theoretical Explanation. SSCI 2023: 501-505 - 2022
- [c5]Luca Sára Pusztaházi, Gábor Csiszár, Michael S. Gashler, Orsolya Csiszár:
Parametric activation functions modelling fuzzy connectives for better explainability of neural models. SISY 2022: 77-82 - [i3]Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár:
Uninorm-like parametric activation functions for human-understandable neural models. CoRR abs/2205.06547 (2022) - 2021
- [b1]József Dombi, Orsolya Csiszár:
Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools. Studies in Fuzziness and Soft Computing 408, Springer 2021, ISBN 978-3-030-72279-1, pp. 3-173 - [j8]Daniel Zeltner, Benedikt Schmid, Gábor Csiszár, Orsolya Csiszár:
Squashing activation functions in benchmark tests: Towards a more eXplainable Artificial Intelligence using continuous-valued logic. Knowl. Based Syst. 218: 106779 (2021) - [j7]Juan G. Díaz Ochoa, Orsolya Csiszár, Thomas Schimper:
Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks. BMC Medical Informatics Decis. Mak. 21(1): 186 (2021) - 2020
- [j6]Orsolya Csiszár, Gábor Csiszár, József Dombi:
Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. Knowl. Based Syst. 199: 105972 (2020) - [j5]Orsolya Csiszár, Gábor Csiszár, József Dombi:
How to implement MCDM tools and continuous logic into neural computation?: Towards better interpretability of neural networks. Knowl. Based Syst. 210: 106530 (2020) - [c4]Julio C. Urenda, Orsolya Csiszár, Gábor Csiszár, József Dombi, György Eigner, Vladik Kreinovich:
Natural Invariance Explains Empirical Success of Specific Membership Functions, Hedge Operations, and Negation Operations. NAFIPS 2020: 443-456 - [c3]Julio C. Urenda, Orsolya Csiszár, Gábor Csiszár, József Dombi, Olga Kosheleva, Vladik Kreinovich, György Eigner:
Why Squashing Functions in Multi-Layer Neural Networks. SMC 2020: 1705-1711 - [i2]Daniel Zeltner, Benedikt Schmid, Gábor Csiszár, Orsolya Csiszár:
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic. CoRR abs/2010.08760 (2020)
2010 – 2019
- 2019
- [c2]Orsolya Csiszár, József Dombi:
Generator-based Modifiers and Membership Functions in Nilpotent Operator Systems. IWOBI 2019: 99-106 - [i1]József Dombi, Orsolya Csiszár, Gábor Csiszár:
Semantic Interpretation of Deep Neural Networks Based on Continuous Logic. CoRR abs/1910.02486 (2019) - 2018
- [c1]József Dombi, Orsolya Csiszár:
Operator-dependent Modifiers in Nilpotent Logical Systems. IJCCI 2018: 126-134 - 2017
- [j4]József Dombi, Orsolya Csiszár:
Self-dual operators and a general framework for weighted nilpotent operators. Int. J. Approx. Reason. 81: 115-127 (2017) - 2016
- [j3]József Dombi, Orsolya Csiszár:
Equivalence operators in nilpotent systems. Fuzzy Sets Syst. 299: 113-129 (2016) - 2015
- [j2]József Dombi, Orsolya Csiszár:
The general nilpotent operator system. Fuzzy Sets Syst. 261: 1-19 (2015) - 2014
- [j1]József Dombi, Orsolya Csiszár:
Implications in bounded systems. Inf. Sci. 283: 229-240 (2014)
Coauthor Index
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