Orsolya Csiszár

According to our database, Orsolya Csiszár authored at least 20 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Parametric Activation Functions for Neural Networks: A Tutorial Survey.

Luca Sára Pusztaházi, György Eigner, Orsolya Csiszár

IEEE Access, 2024

2023

Bayesian logical neural networks for human-centered applications in medicine.

Juan G. Díaz Ochoa, Lukas Maier, Orsolya Csiszár

Frontiers Bioinform., May, 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

Why Fuzzy Control Is Often More Robust (and Smoother): A Theoretical Explanation.

Orsolya Csiszár, Gábor Csiszár, Olga Kosheleva, Martine Ceberio, Vladik Kreinovich

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

2022

Parametric activation functions modelling fuzzy connectives for better explainability of neural models.

Luca Sára Pusztaházi, Gábor Csiszár, Michael S. Gashler, Orsolya Csiszár

Proceedings of the 20th Jubilee International Symposium on Intelligent Systems and Informatics, 2022

2021

Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools

József Dombi, Orsolya Csiszár

Studies in Fuzziness and Soft Computing 408, Springer, ISBN: 978-3-030-72279-1, 2021

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks.

Juan G. Díaz Ochoa, Orsolya Csiszár, Thomas Schimper

BMC Medical Informatics Decis. Mak., 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

2020

How to implement MCDM tools and continuous logic into neural computation?: Towards better interpretability of neural networks.

Orsolya Csiszár, Gábor Csiszár, József Dombi

Knowl. Based Syst., 2020

Interpretable neural networks based on continuous-valued logic and multicriteria decision operators.

Orsolya Csiszár, Gábor Csiszár, József Dombi

Knowl. Based Syst., 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

Why Squashing Functions in Multi-Layer Neural Networks.

Julio C. Urenda, Orsolya Csiszár, Gábor Csiszár, József Dombi, Olga Kosheleva, Vladik Kreinovich, György Eigner

Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020

Natural Invariance Explains Empirical Success of Specific Membership Functions, Hedge Operations, and Negation Operations.

Julio C. Urenda, Orsolya Csiszár, Gábor Csiszár, József Dombi, György Eigner, Vladik Kreinovich

Proceedings of the Fuzzy Information Processing 2020, 2020

2019

Semantic Interpretation of Deep Neural Networks Based on Continuous Logic.

József Dombi, Orsolya Csiszár, Gábor Csiszár

CoRR, 2019

Generator-based Modifiers and Membership Functions in Nilpotent Operator Systems.

Orsolya Csiszár, József Dombi

Proceedings of the IEEE International Work Conference on Bioinspired Intelligence, 2019

2018

Operator-dependent Modifiers in Nilpotent Logical Systems.

József Dombi, Orsolya Csiszár

Proceedings of the 10th International Joint Conference on Computational Intelligence, 2018

2017

Self-dual operators and a general framework for weighted nilpotent operators.

József Dombi, Orsolya Csiszár

Int. J. Approx. Reason., 2017

2016

Equivalence operators in nilpotent systems.

József Dombi, Orsolya Csiszár

Fuzzy Sets Syst., 2016

2015

The general nilpotent operator system.

József Dombi, Orsolya Csiszár

Fuzzy Sets Syst., 2015

2014

Implications in bounded systems.

József Dombi, Orsolya Csiszár

Inf. Sci., 2014