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.
5 code implementations • 20 Feb 2019 • Rahul Yedida, Snehanshu Saha, Tejas Prashanth
In this paper, we propose a novel method to compute the learning rate for training deep neural networks with stochastic gradient descent.