Improving neural network efficiency through Adam optimization and l₂, l₀ regularization
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ID: 286835
2025
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Abstract
Neural networks offer exceptional predictive power, but their high computational demands pose challenges for deployment in technologies with limited computing power. Researchers have recently proposed a form of regularization based on a combination of the l2 and l0 norms that increases the zero entries in the weight matrices which would result in simpler computations. In this study, we extended the application of the scheme to the Adaptive Moments Estimation optimization algorithm in order to create a more efficient algorithm, one that creates lightweight models with shorter training times in order to reap greater efficiency benefits. To test the effect of the modification to the algorithm, we trained models on a breast cancer malignancy dataset using both gradient descent and Adam optimizers, with and without the regularization terms. Our findings show that integrating the regularization scheme with Adam yielded sparser neural networks with faster training times compared to gradient descent while maintaining model performance.
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| Authors | Reynoso, Markus Nikolo C. |
| Journal | Malay Journal |
| Year | 2025 |
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| Keywords | Keywords not found |
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