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1 Ergebnisse
1
Toward Certified Robustness Against Real-World Distribution..:
, In:
2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
,
Wu, Haoze
;
Tagomori, Teruhiro
;
Robey, Alexander
... - p. 537-553 , 2023
Link:
https://doi.org/10.1109/SaTML54575.2023.00042
RT T1 <cTitle>2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)</cTitle>: T1 <Title>Toward Certified Robustness Against Real-World Distribution Shifts</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10136136&Exemplar=1&LAN=DE A1 Wu, Haoze A1 Tagomori, Teruhiro A1 Robey, Alexander A1 Yang, Fengjun A1 Matni, Nikolai A1 Pappas, George A1 Hassani, Hamed A1 Pasareanu, Corina A1 Barrett, Clark YR 2023 K1 Deep learning K1 Machine learning algorithms K1 Perturbation methods K1 Neural networks K1 Robustness K1 Data models K1 Formal specifications K1 certified robustness K1 distribution shift K1 generative models K1 S-shaped activations K1 CEGAR SP 537 OP 553 LK http://dx.doi.org/https://doi.org/10.1109/SaTML54575.2023.00042 DO https://doi.org/10.1109/SaTML54575.2023.00042 SF ELIB - SuUB Bremen
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