Accelerated Bayesian optimization for CNN+LSTM learning rate tuning via precomputed Gaussian process subspaces in soil analysis

Accelerated Bayesian optimization for CNN+LSTM learning rate tuning via precomputed Gaussian process subspaces in soil analysis

PurposeWe propose an accelerated Bayesian optimization framework for tuning the learning rate of CNN+LSTM models in soil analysis, addressing the computational inefficiency of traditional Gaussian Process (GP)-based methods. This work bridges the gap between computational efficiency and probabilisti...

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Bibliographic Details
Main Authors:	Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong, Zhengchun Song
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2025-08-01
Series:	Frontiers in Environmental Science
Subjects:	Bayesian optimization CNN+LSTM soil analysis Gaussian process computational efficiency hyperparameter tuning
Online Access:	https://www.frontiersin.org/articles/10.3389/fenvs.2025.1633046/full
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