GASGD: stochastic gradient descent for distributed asynchronous matrix completion via graph partitioning.

By Fabio Petroni, Leonardo Querzoni. 2014

27 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ji Liu
2. Xiangru Lian
3. Cho-Jui Hsieh
4. Huan Zhang
5. Yijun Huang
6. Ce Zhang
7. Tong Zhang
8. Yuncheng Li

In Computer Networks and Communications

1. Ji Liu
2. Xiangru Lian
3. Cho-Jui Hsieh
4. Yuncheng Li
5. Yijun Huang
6. Huan Zhang
7. Jianyu Wang
8. Hanlin Tang

In Information Systems

1. Cho-Jui Hsieh
2. Xiangru Lian
3. Ji Liu
4. Yijun Huang
5. Huan Zhang

Citations in the corpus

• Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization (2015, background): “…witnessed in many applications such as deep neural network [Dean et al., 2012, Paine et al., 2013, Zhang et al., 2014, Li et al., 2014a], matrix completion [Niu et al., 2011, Petroni and Querzoni, 2014, Yun et al., 2013], SVM [Niu et al., 2011], and solving linear equations [Liu et al., 2014b].”
• A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order (2016, methodology): “This paper provides a comprehensive and generic analysis to study the speedup property for a broad range of asynchronous parallel stochastic algorithms from the zeroth order to the first order methods.”