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Understanding soft error sensitivity of deep learning models and frameworks through checkpoint alteration

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dc.contributor.authorRojas, Elvis
dc.contributor.authorPerez, Diego
dc.contributor.authorCalhoun, Jon C.
dc.contributor.authorBautista-Gómez, Leonardo
dc.contributor.authorJones, Terry
dc.contributor.authorMeneses, Esteban
dc.date.accessioned2026-08-11T14:28:25Z
dc.date.available2026-08-11T14:28:25Z
dc.date.issued2021
dc.identifier.doihttps://doi.org/10.1109/Cluster48925.2021.00045
dc.identifier.urihttps://hdl.handle.net/20.500.12337/11836
dc.language.isoen_US
dc.publisherProceedings IEEE International Conference on Cluster Computing
dc.rightsacceso abierto
dc.sourceProceedings IEEE International Conference on Cluster Computing, pp.492-503, 2021
dc.subjectAPRENDIZAJE PROFUNDO
dc.subjectCOMPUTACIÓN DE ALTO RENDIMIENTO
dc.subjectREDES NEURONALES
dc.subjectCOMPUTADORES
dc.titleUnderstanding soft error sensitivity of deep learning models and frameworks through checkpoint alteration
dc.typeartículo de investigación

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