Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
arXiv:2403.05532v2 Announce Type: replace Abstract: We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process. Twin leverages the margin-maximization dynamics of homogeneous networks and an empirical scaling law that links training and test losses across hyper-parameter configurations. This mathematical modeling yields a regime-dependent, validation-free selection rule: in the non-separable regime, training loss
The increasing complexity and computational cost of deep learning models necessitate more efficient methods for hyperparameter tuning, which Twin addresses by bypassing validation sets.
This innovation significantly streamlines the training process for deep homogeneous classifiers, reducing computational overhead and accelerating AI research and development cycles.
The ability to tune crucial hyperparameters like learning rate and weight decay without a validation set eliminates a common bottleneck, making model development faster and potentially more robust.
- · AI researchers
- · Deep learning practitioners
- · Cloud computing providers (reduced computation time)
- · AI product development teams
- · Traditional hyperparameter optimization tools heavily reliant on validation sets
Faster iteration times in AI model development for specific architectures.
Accelerated deployment of new AI capabilities due to reduced development friction.
Increased accessibility and efficiency in developing complex AI systems, potentially democratizing advanced AI development further.
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Read at arXiv cs.LG