An Adaptive Remote Stochastic Gradient Method for Training Neural Networks

By Yushu Chen, Hao Jing, Wenlai Zhao, Zhiqiang Liu, Ouyi Li, Li'an Qiao, Wei Xue, Haohuan Fu, Guangwen Yang. 2019

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

1. Christopher J. Shallue
2. Chris J. Maddison
3. Dami Choi
4. George E. Dahl
5. Jaehoon Lee
6. Zachary Nado
7. Yann LeCun
8. Samuel L. Smith

Citations in the corpus

• On Empirical Comparisons of Optimizers for Deep Learning (2019, background): “…(cid:15) at the common default value of 10 − 8 for Adam and 10 − 10 for RMSProp (Tieleman and Hinton, 2012; Kingma and Ba, 2015; Dozat, 2016; Balles and Hennig, 2017; Loshchilov and Hutter, 2017; Zou and Shen, 2018; Ma and Yarats, 2018; Bernstein et al., 2018; Chen et al., 2019; Zou et al., 2019).”