Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Bielefeld, Universität Bielefeld, Dissertation, 2022
- Schlagwort
-
Robustheit
Maschinelles Lernen
Deep learning
Neuronales Netz
- Ereignis
-
Veröffentlichung
- (wo)
-
Bielefeld
- (wer)
-
Universitätsbibliothek Bielefeld
- (wann)
-
2022
- Urheber
- DOI
-
10.4119/unibi/2967691
- URN
-
urn:nbn:de:0070-pub-29676913
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:49 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Göpfert, Jan Philip
- Universitätsbibliothek Bielefeld
Entstanden
- 2022
Ähnliche Objekte (12)
Adversarial attacks and adversarial robustness in computational pathology
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 Robustness of Graph Neural Networks
Advances in Reliably Evaluating and Improving Adversarial Robustness
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
Adversarial content manipulation for analyzing and improving model robustness
Example-based explanations with adversarial attacks for respiratory sound analysis
Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications
Evaluating and Improving the Robustness of Image Classifiers against Adversarial Attacks
GANterfactual - counterfactual explanations for medical non-experts using generative adversarial learning
Co‐adaptation impacts the robustness of predator–prey dynamics against perturbations
Adversarial attacks and adversarial robustness in computational pathology
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 Robustness of Graph Neural Networks
Advances in Reliably Evaluating and Improving Adversarial Robustness
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
Adversarial content manipulation for analyzing and improving model robustness
Example-based explanations with adversarial attacks for respiratory sound analysis
Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications
Evaluating and Improving the Robustness of Image Classifiers against Adversarial Attacks
GANterfactual - counterfactual explanations for medical non-experts using generative adversarial learning
Co‐adaptation impacts the robustness of predator–prey dynamics against perturbations
Adversarial attacks and adversarial robustness in computational pathology
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 Robustness of Graph Neural Networks
Advances in Reliably Evaluating and Improving Adversarial Robustness
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
Adversarial content manipulation for analyzing and improving model robustness
Example-based explanations with adversarial attacks for respiratory sound analysis
Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications
Evaluating and Improving the Robustness of Image Classifiers against Adversarial Attacks
GANterfactual - counterfactual explanations for medical non-experts using generative adversarial learning