Curvature-Adaptive Learning Rate Optimizer: Theoretical Insights and Empirical Evaluation on Neural Network Training
Optimizing neural networks often encounters challenges such as saddle points, plateaus, and ill-conditioned curvature, limiting the effectiveness of standard optimizers like Adam, Nadam, and RMSProp. To address these limitations, we propose the Curvature-Adaptive Learning Rate (CALR) optimizer, a n...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138986 |
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