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

no code implementations • 19 May 2020 • Snehanshu Saha, Tejas Prashanth, Suraj Aralihalli, Sumedh Basarkod, T. S. B Sudarshan, Soma S. Dhavala

We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regression tasks.

regression