Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
arXiv:2607.08511v1 Announce Type: new 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 acro
The proliferation of diverse neural network architectures and the increasing computational cost of AI training necessitate more efficient hyperparameter optimization techniques.
Optimizing learning rate scheduling can significantly improve the efficiency and performance of deep learning models, accelerating AI development and reducing computational overhead.
The systematic evaluation provides a clearer understanding of how different learning rate strategies impact model performance across varied architectures, potentially leading to more automated and effective training protocols.
- · AI researchers
- · Deep learning practitioners
- · Cloud computing providers
- · AI model developers
- · Organizations with inefficient hyperparameter tuning processes
Improved classification accuracy and training efficiency across a range of neural network models.
Reduced computational costs for training large AI models, making advanced AI more accessible.
Accelerated development of domain-specific AI applications due to faster and more reliable model training.
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