Selected publications

• Pócoš, Š., Bečková, I., and Farkaš, I. (2024). RecViT: Enhancing vision transformer with top-down information flow. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, pages 749–756. INSTICC, SciTePress.
• Krakovská, A., Pócoš, Š., Mojžišová, K., Bečková, I., and Gubáš, J. X. (2022). State space reconstruction techniques and the accuracy of prediction. Communications in Nonlinear Science and Numerical Simulation, 111:106422.
• Pócoš, Š., Bečková, I., and Farkaš, I. (2022). Examining the proximity of adversarial examples to class manifolds in deep networks. In Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., and Aydin, M., editors, Artificial Neural Networks and Machine Learning – ICANN 2022, pages 645–656, Cham. Springer Nature Switzerland.
• Bečková, I., Pócoš Š., and Farkaš, I. (2020). Computational analysis of robustness in neural network classifiers. In Farkaš, I., Masulli, P., and Wermter, S., editors, Artificial Neural Networks and Machine Learning – ICANN 2020, pages 65–76, Cham. Springer International Publishing.
• Pócoš, Š., Bečková, I., Kuzma, T., and Farkaš, I. (2021). Assessment of manifold unfolding in trained deep neural network classifiers. In Heintz, F., Milano, M., and O’Sullivan, B., editors, Trustworthy AI - Integrating Learning, Optimization and Reasoning, pages 93–103, Cham. Springer International Publishing.

VUPCH

Timetable

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