Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
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Computer Science > Machine Learning
Title:Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
Abstract:Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we systematically investigate its impact on classification accuracy across a diverse pool of architectures. We evaluated 30 representative architectures from convolutional and transformer families within the LEMUR neural network dataset. Through automated source-code injection, we applied 25 scheduler configurations across nine PyTorch families, evaluating a total of 3,938 model variants on CIFAR-10. Our best configuration achieved a top-1 accuracy of 86.45%, with 237 variants exceeding 80%. The results show that the choice of scheduler depends heavily on the architecture: CosineAnnealingWarmRestarts and CyclicLR consistently outperform basic decay strategies. The resulting accuracy landscape, contributed to the LEMUR nn-dataset, provides a practical reference for principled scheduler selection.
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.08511 [cs.LG] |
| (or arXiv:2607.08511v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08511
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Hafsa Mateen HafsaMateen [view email][v1] Thu, 9 Jul 2026 14:06:39 UTC (251 KB)
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