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Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
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 ac
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