A Meta-learning-assisted Training Framework with Confidence Interval for Optical Network Modeling

Qizhi Qiu; Xiaomin Liu; Yihao Zhang; Lilin Yi; Weisheng Hu; Qunbi Zhuge

doi:10.1364/NETWORKS.2021.NeF2B.1

OSA Advanced Photonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper NeF2B.1
•https://doi.org/10.1364/NETWORKS.2021.NeF2B.1

A Meta-learning-assisted Training Framework with Confidence Interval for Optical Network Modeling

Qizhi Qiu, Xiaomin Liu, Yihao Zhang, Lilin Yi, Weisheng Hu, and Qunbi Zhuge

Photonic Networks and Devices 2021

Washington, DC United States
26–29 July 2021
ISBN: 978-1-943580-94-1

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Machine Learning and Network Monitoring (NeF2B)

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Q. Qiu, X. Liu, Y. Zhang, L. Yi, W. Hu, and Q. Zhuge, "A Meta-learning-assisted Training Framework with Confidence Interval for Optical Network Modeling," in OSA Advanced Photonics Congress 2021, B. Yang, F. Leo, A. Tauke-Pedretti, S. Arafin, M. Clerici, L. Caspani, L. Busse, M. Kats, J. Fan, W. Ming (Steve) Lee, D. Caplan, N. Parsons, W. Freude, C. Tremblay, D. Hillerkuss, K. Igarashi, H. Elgala, N. Guerrero Gonzalez, R. "Patty" Stabile, K. Jaeger, R. Saive, H. Aziz, and H. Tan, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper NeF2B.1.

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Abstract

A reliable and accurate meta-learning-assisted training framework is proposed for deploying machine-learning-based applications to optical networks. The framework provides prediction confidence interval and makes better utilization of noisy data.

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