Runtime Analysis of Stochastic Gradient Descent-Reference-Cited by-同舟云学术

Runtime Analysis of Stochastic Gradient Descent

Published:2020-10-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 4th International Conference on Computer Science and Application Engineering
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Author:

Hu Guanqiang¹,Zhang Yushan²

Affiliation:

1. School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, China

2. School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, China and Big Data and Educational Statistics Application Laboratory, Guangdong University of Finance and Economics, Guangzhou, China

Funder

National Natural Science Foundation of China

Science and Technology Program of Guangzhou

Guangdong Basic and Applied Basic Research Foundation

Publisher

ACM

Reference34 articles.

1. Aymeric Dieuleveut Alain Durmus and Francis Bach (2017). Bridging the gap between constant step size stochastic gradient descent and markov chains. arXiv preprint arXiv:1707.06386 2017. Aymeric Dieuleveut Alain Durmus and Francis Bach (2017). Bridging the gap between constant step size stochastic gradient descent and markov chains. arXiv preprint arXiv:1707.06386 2017.

2. Tom Schaul and Yann LeCun (2013). Adaptive learning rates and parallelization for stochastic sparse non-smooth gradients. arXiv preprint arXiv:1301.3764 2013. Tom Schaul and Yann LeCun (2013). Adaptive learning rates and parallelization for stochastic sparse non-smooth gradients. arXiv preprint arXiv:1301.3764 2013.

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