AdamZ: an enhanced optimisation method for neural network training

Zaznov, I., Badii, A., Kunkel, J. and Dufour, A. ORCID: https://orcid.org/0000-0003-0519-648X (2025) AdamZ: an enhanced optimisation method for neural network training. Neural Computing and Applications. ISSN 1433-3058 doi: 10.1007/s00521-025-11649-w

Abstract/Summary

AdamZ is an advanced variant of the Adam optimiser, developed to enhance convergence efficiency in neural network training. This optimiser dynamically adjusts the learning rate by incorporating mechanisms to address overshooting and stagnation, which are common challenges in optimisation. Specifically, AdamZ reduces the learning rate when overshooting is detected and increases it during periods of stagnation, utilising hyperparameters such as overshoot and stagnation factors, thresholds, and patience levels to guide these adjustments. While AdamZ may lead to slightly longer training times compared to some other optimisers, it consistently excels in minimising the loss function, making it particularly advantageous for applications where precision is critical. Benchmarking results demonstrate the effectiveness of AdamZ in maintaining optimal learning rates, leading to improved model performance across diverse tasks.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/124435
Identification Number/DOI	10.1007/s00521-025-11649-w
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer
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