Accelerating Stochastic Training for Over-parametrized Learning
By Chaoyue Liu, Mikhail Belkin. 2018
3 citations recorded by the source corpus.
Subfields: Optimization & Training
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Optimization & Training
- Simon Lacoste-Julien
- Issam Hadj Laradji
- Gauthier Gidel
- Sharan Vaswani
- Nicolas Loizou
- Peter Richtárik
- Mark W. Schmidt
- Aaron Mishkin
Citations in the corpus
- Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates (2019, background): “Indeed, recent works [5, 13, 33, 47, 52, 73, 83] have shown that when training over-parameterized models, classic SGD with a constant step-size and without VR can achieve the convergence rates of full-batch gradient descent.”