Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates

23 February 2024

Papers citing "Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates"

8 / 8 papers shown

Title
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 45 182 0 09 Feb 2020
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Aaron Mishkin I. Laradji Mark Schmidt Gauthier Gidel Simon Lacoste-Julien ODL 72 208 0 24 May 2019
Convergence rates for the stochastic gradient descent method for non-convex objective functions Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen 61 101 0 02 Apr 2019
Measuring the Effects of Data Parallelism on Neural Network Training Christopher J. Shallue Jaehoon Lee J. Antognini J. Mamou J. Ketterling Yao Wang 78 408 0 08 Nov 2018
Lipschitz regularity of deep neural networks: analysis and efficient estimation Kevin Scaman Aladin Virmaux 64 523 0 28 May 2018
Don't Decay the Learning Rate, Increase the Batch Size Samuel L. Smith Pieter-Jan Kindermans Chris Ying Quoc V. Le ODL 95 990 0 01 Nov 2017
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 100 1,538 0 22 Sep 2013
Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter Ohad Shamir Nathan Srebro Karthik Sridharan ODL 108 313 0 22 Jun 2011