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Titel:

Adversarial Network Algorithm Benchmarking

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Lettner, Sebastian; Blenk, Andreas
Abstract:
Most research papers should have one thing in common: a clear and expressive evaluation of proposed solutions to problems. However, evaluating solutions is interestingly a challenging task: when using human-constructed examples or real-world data, it is difficult to assess to which degree the data represents the input spectrum also of future demands. Moreover, evaluations which fail to show generalization might hide algorithm weak-spots, which could eventually lead to reliability and security is...     »
Stichworte:
Adversarial Traffic Generation, Artificial Intelligence, Data Center
Horizon 2020:
647158
Kongress- / Buchtitel:
The 15th International Conference on emerging Networking EXperiments and Technologies (CoNEXT ’19 Companion)
Datum der Konferenz:
December 9-12
Verlag / Institution:
ACM
Jahr:
2019
Jahr / Monat:
2019-12
Monat:
Dec
Seiten:
3
Volltext / DOI:
doi:10.1145/3360468.3366779
Semester:
WS 19-20
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