arXiv cs.CV
· Papers
Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
arXiv:2607.08511v1 Announce Type: cross 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