Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
Impact in
Classified as
- Authors
- Rie JohnsonTong Zhang
- Journal
- Rare & Special e-Zone (The Hong Kong University of Science and Technology)
In The Last Decade
doi.org/w4545939 →Countries where authors are citing Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
This map shows the geographic impact of Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Accelerating Stochastic Gradient Descent using Predictive Variance Reduction with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Accelerating Stochastic Gradient Descent using Predictive Variance Reduction more than expected).
Fields of papers citing Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
This network shows the impact of Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Accelerating Stochastic Gradient Descent using Predictive Variance Reduction.
About Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
This paper, published in 2013, received 809 indexed citations . Written by Rie Johnson and Tong Zhang covering the research area of Artificial Intelligence and Computational Mechanics. It is primarily cited by scholars working on Artificial Intelligence (669 citations), Computational Mechanics (395 citations), Computer Vision and Pattern Recognition (145 citations), Computer Networks and Communications (98 citations) and Statistics and Probability (88 citations). Published in Rare & Special e-Zone (The Hong Kong University of Science and Technology).
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This paper is also available at doi.org/w4545939.