A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems
By Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin. 2015
108 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
- Huan Zhang
- Cho-Jui Hsieh
- Kumar Kshitij Patel
- Aymeric Dieuleveut
- Venkatesh Akella
- Sebastian U. Stich
- Pin-Yu Chen
- Martin Jaggi
In Computer Networks and Communications
- Cho-Jui Hsieh
- Aymeric Dieuleveut
- Kumar Kshitij Patel
- Huan Zhang
- Ji Liu
- Jianyu Wang
- Ce Zhang
- Hanlin Tang
In Information Systems
Citations in the corpus
- Communication trade-offs for synchronized distributed SGD with large step size (2019, background): “There has also been work in parallelizing stochastic optimization algorithms for specific problems (like PCA) in the past, for e.g., Mcdonald et al. (2009); McDonald et al. (2010); Meng et al. (2012); Zhuang et al. (2013); Li et al. (2014a); Chin et al. (2015); Oh et al. (2015).”
- HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent (2016, methodology): “This simple but effective idea has become extremely popular and used in many other applications, such as asynchronous coordinate descent [13], [6], Pagerank [14] and matrix completion [3].”