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 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
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- Linked via arxiv authorHafsa Mateen →
Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
- Linked via arxiv authorRadu Timofte →
Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
- Linked via arxiv authorDmitry Ignatov →
Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures