Tejas Prashanth

LALR: Theoretical and Experimental validation of Lipschitz Adaptive\n Learning Rate in Regression and Neural Networks

2020 · arXiv (Cornell University)

We propose a theoretical framework for an adaptive learning rate policy for\nthe Mean Absolute Error loss function and Quantile loss function and evaluate\nits effectiveness for regression tasks. The framework is based on the theory of\nLipschitz …