- Alternative title
-
Robustheit von Graph-Neuronalen Netzwerken gegenüber Feindlichen Angriffen
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
München, Technische Universität München, Dissertation, 2022
- Event
-
Veröffentlichung
- (where)
-
München
- (who)
-
Universitätsbibliothek der TU München
- (when)
-
2022
- Creator
-
Zügner, Daniel
- Contributor
- URN
-
urn:nbn:de:bvb:91-diss-20220518-1641938-1-8
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:53 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zügner, Daniel
- Günnemann, Stephan
- Papalexakis, Evangelos
- Universitätsbibliothek der TU München
Time of origin
- 2022
Other Objects (12)
Structack : Structure-based Adversarial Attacks on Graph Neural Networks
Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels
Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels
Adversarial attacks and adversarial robustness in computational pathology
Robustness and applications of cellular neural networks
Latent-based adversarial neural networks for facial affect estimations
Explaining Graph Neural Networks
Neural and biochemical networks : organization, development, and robustness
Automatic Short Answer Grading Using Neural Models. Examining Adversarial Robustness and Elaborated Feedback Generation
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Advances in Reliably Evaluating and Improving Adversarial Robustness
Towards robustifying deep neural networks against adversarial, fringe and distorted examples
Structack : Structure-based Adversarial Attacks on Graph Neural Networks
Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels
Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels
Adversarial attacks and adversarial robustness in computational pathology
Robustness and applications of cellular neural networks
Latent-based adversarial neural networks for facial affect estimations
Explaining Graph Neural Networks
Neural and biochemical networks : organization, development, and robustness
Automatic Short Answer Grading Using Neural Models. Examining Adversarial Robustness and Elaborated Feedback Generation
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Advances in Reliably Evaluating and Improving Adversarial Robustness
Towards robustifying deep neural networks against adversarial, fringe and distorted examples
Structack : Structure-based Adversarial Attacks on Graph Neural Networks
Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels
Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels
Adversarial attacks and adversarial robustness in computational pathology
Robustness and applications of cellular neural networks
Latent-based adversarial neural networks for facial affect estimations
Explaining Graph Neural Networks
Neural and biochemical networks : organization, development, and robustness
Automatic Short Answer Grading Using Neural Models. Examining Adversarial Robustness and Elaborated Feedback Generation
Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
Advances in Reliably Evaluating and Improving Adversarial Robustness