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- W3119666095 abstract "We propose a two-stage model called Depth Self-Optimized Learning (DSOL), which aims to realize ANN depth self-configuration, self-optimization as well as ANN training without manual intervention. In the first stage of DSOL, it will configure ANN of specific depths according to a specific dataset. In the second stage, DSOL will continuously optimize ANN based on Reinforcement Learning (RL). Finally, the optimal depth is returned to the first stage of DSOL for training, so that DSOL can configure the appropriate ANN depth and perform more reasonable optimization when processing similar datasets again. In the experiment, we ran DSOL on the Iris and Boston housing datasets, and the results showed that DSOL performed well. We have uploaded the experiment records and code to our Github ( https://github.com/workharduwillwin/Depth-Self-Optimized-Learning-Toward-Data-Science )." @default.
- W3119666095 created "2021-01-18" @default.
- W3119666095 creator A5053944287 @default.
- W3119666095 date "2021-01-01" @default.
- W3119666095 modified "2023-10-16" @default.
- W3119666095 title "Depth Self-optimized Learning Toward Data Science" @default.
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- W3119666095 doi "https://doi.org/10.1007/978-3-030-80126-7_39" @default.
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