LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence

20 February 2019

Papers citing "LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence"

5 / 5 papers shown

Title
Machine learning tools to improve nonlinear modeling parameters of RC columns Hamid Khodadadi Koodiani Elahe Jafari Arsalan Majlesi Mohammad Shahin A. Matamoros A. Alaeddini 14 9 0 09 Mar 2023
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence Rohan Mohapatra Snehanshu Saha C. Coello Anwesh Bhattacharya S. Dhavala S. Saha ODL 18 21 0 19 May 2020
CProp: Adaptive Learning Rate Scaling from Past Gradient Conformity Konpat Preechakul B. Kijsirikul ODL 28 3 0 24 Dec 2019
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets Snehanshu Saha N. Nagaraj Mathur Archana Rahul Yedida 14 9 0 01 Jun 2019
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay L. Smith 208 1,019 0 26 Mar 2018