Out-of-field generic ML training with in-field specific adaptation to facilitate ML deployments
Visualitza/Obre
Cita com:
hdl:2117/169773
Tipus de documentText en actes de congrés
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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ProjecteMETRO-HAUL - METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency (EC-H2020-761727)
COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION (AEI-TEC2017-90097-R)
COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION (AEI-TEC2017-90097-R)
Abstract
A two-phase strategy to facilitate ML algorithm deployment in real networks is demonstrated: out-of-field training uses data from simulation and testbed experiments with generic equipment whereas in-field adaptation is applied to support heterogeneous equipment.
Descripció
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CitacióShariati, M.; Ruiz, M.; Velasco, L. Out-of-field generic ML training with in-field specific adaptation to facilitate ML deployments. A: Optical Fiber Communications Conference and Exposition. "Optical Fiber Communication Conference 2019, San Diego, California, United States, 3-7 March 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, article 8696353, p. 1-3.
ISBN978-1-943580-53-8
Versió de l'editorhttps://ieeexplore.ieee.org/document/8696353
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[OFC] Signal residuals.pdf | 170,8Kb | Visualitza/Obre |