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

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

19 May 2020
Snehanshu Saha
Tejas Prashanth
Suraj Aralihalli
Sumedh Basarkod
T. Sudarshan
S. Dhavala
ArXiv (abs)PDFHTML

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

2 / 2 papers shown
Title
Estimation and Applications of Quantiles in Deep Binary Classification
Estimation and Applications of Quantiles in Deep Binary Classification
Anuj Tambwekar
Anirudh Maiya
S. Dhavala
Snehanshu Saha
UQCV
32
7
0
09 Feb 2021
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with
  Swarm Intelligence
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
Rohan Mohapatra
Snehanshu Saha
C. Coello
Anwesh Bhattacharya
S. Dhavala
S. Saha
ODL
72
21
0
19 May 2020
1