VR-SGD: A Simple Stochastic Variance Reduction Method for Machine
Learning

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

26 February 2018

Ivor W. Tsang

Papers citing "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning"

10 / 10 papers shown

Title
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 42 6 0 09 Jan 2023
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity A. Davtyan Sepehr Sameni L. Cerkezi Givi Meishvili Adam Bielski Paolo Favaro ODL 55 2 0 07 Jul 2021
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning Hua Huang Fanhua Shang Yuanyuan Liu Hongying Liu FedML 29 14 0 23 Jun 2021
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers Robin M. Schmidt Frank Schneider Philipp Hennig ODL 40 162 0 03 Jul 2020
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning Pengzhan Guo Zeyang Ye Keli Xiao Wei Zhu 24 14 0 07 Apr 2020
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 27 11 0 15 Sep 2019
Double Quantization for Communication-Efficient Distributed Optimization Yue Yu Jiaxiang Wu Longbo Huang MQ 19 57 0 25 May 2018
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$ Yi Tian Xu Yan Yan Qihang Lin Tianbao Yang 52 25 0 13 Jul 2016
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 108 1,157 0 04 Mar 2015
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014