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- W4285789076 abstract "It is possible to select a set of potential candidate machine learning (ML) models based on 5G use-case requirements and characteristics of the ML model, however, it is extremely difficult to predict the best model right at the start. This work proposes an automated ML framework called automated 5G machine learning (Auto5GML), which can be integrated with the unified ML architecture, by the International Telecommunication Union (ITU). Based on the potential search space of ML models, the Auto5GML framework selects the best model to be used for a 5G use-case. It evaluates the potential models by inserting data and running them in parallel by using data parallelism or model parallelism. The proposed framework can optimize the learning performance based on the strict use-case requirements." @default.
- W4285789076 created "2022-07-19" @default.
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- W4285789076 date "2019-09-01" @default.
- W4285789076 modified "2023-10-07" @default.
- W4285789076 title "Automated Machine Learning for Future Networks Including 5G" @default.
- W4285789076 doi "https://doi.org/10.1109/ntpe.2019.9778120" @default.
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