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Please use this identifier to cite or link to this item: https://hdl.handle.net/10923/18178
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dc.contributor.authorAjitesh Srivastava-
dc.contributor.authorTa-Yang Wang-
dc.contributor.authorPengmiao Zhang-
dc.contributor.authorCesar Augusto Fonticielha De Rose-
dc.contributor.authorRajgopal Kannan-
dc.contributor.authorViktor K. Prasanna-
dc.date.accessioned2021-09-01T13:14:26Z-
dc.date.available2021-09-01T13:14:26Z-
dc.date.issued2020-
dc.identifier.isbn1611-3349-
dc.identifier.urihttps://hdl.handle.net/10923/18178-
dc.language.isoen-
dc.relation.ispartofProceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2020, Cingapura.-
dc.rightsopenAccess-
dc.subjectGerência de Recursos-
dc.subjectEscalonamento de Recursos-
dc.subjectInteligência Artificial (IA)-
dc.titleMemMAP: Compact and Generalizable Meta-LSTM Models for Memory Access Prediction-
dc.typeconferenceObject-
dc.date.updated2021-09-01T13:14:25Z-
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