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Conference Paper: POPQORN: Quantifying Robustness of Recurrent Neural Networks

TitlePOPQORN: Quantifying Robustness of Recurrent Neural Networks
Authors
Ko, Ching-YunLyu, ZhaoyangWeng, LilyDaniel, LucaWong, NgaiLin, Dahua
Issue Date10-Jun-2019
Persistent Identifierhttp://hdl.handle.net/10722/339474

 

DC FieldValueLanguage
dc.contributor.authorKo, Ching-Yun-
dc.contributor.authorLyu, Zhaoyang-
dc.contributor.authorWeng, Lily-
dc.contributor.authorDaniel, Luca-
dc.contributor.authorWong, Ngai-
dc.contributor.authorLin, Dahua -
dc.date.accessioned2024-03-11T10:36:56Z-
dc.date.available2024-03-11T10:36:56Z-
dc.date.issued2019-06-10-
dc.identifier.urihttp://hdl.handle.net/10722/339474-
dc.languageeng-
dc.relation.ispartof36th International Conference on Machine Learning (10/06/2019-15/06/2019, , , Long Beach Convention Center, Long Beach)-
dc.titlePOPQORN: Quantifying Robustness of Recurrent Neural Networks-
dc.typeConference_Paper-
dc.identifier.issue97-

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