Adaptive Step-Size Methods for Compressed SGD
Author:
Affiliation:
1. University of Illinois at Urbana-Champaign,ECE Department and Coordinated Science Lab
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096611.pdf?arnumber=10096611
Reference19 articles.
1. SVRG meets adagrad: Painless variance reduction;dubois-taine;CoRR,2021
2. Painless stochastic gradient: Interpolation, line-search, and convergence rates;vaswani;CoRR,2019
3. On exponential convergence of sgd in non-convex over-parametrized learning;bassily;arXiv preprint arXiv 1811 02564,2018
4. Minimization of functions having Lipschitz continuous first partial derivatives
5. Adaptive step size selection for optimization via the ski rental problem
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Adaptive Step-Size Methods for Compressed SGD With Memory Feedback;IEEE Transactions on Signal Processing;2024
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