Double-Bayesian Framework Optimizes Neural Network Learning Rates

A novel probabilistic approach has been introduced for enhancing learning rates during neural network training. This method builds upon traditional Bayesian statistics, incorporating a dual-Bayesian decision framework that features two opposing Bayesian processes. From this, an optimal learning rate for stochastic gradient descent can be theoretically established. This innovation tackles the persistent issue of hyperparameter selection, aiming to prevent overfitting and ensure unbiased results, which has typically depended on empirical testing. Validation through experiments in classification, segmentation, and detection tasks highlights the method's practical relevance. The research paper can be accessed on arXiv with the identifier 2605.20009.