Parallelized Stochastic Gradient Descent
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w8244415 →Countries where authors are citing Parallelized Stochastic Gradient Descent
This map shows the geographic impact of Parallelized Stochastic Gradient Descent. 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 Parallelized Stochastic Gradient Descent with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parallelized Stochastic Gradient Descent more than expected).
Fields of papers citing Parallelized Stochastic Gradient Descent
This network shows the impact of Parallelized Stochastic Gradient Descent. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Parallelized Stochastic Gradient Descent.
About Parallelized Stochastic Gradient Descent
This paper, published in 2010, received 623 indexed citations . Written by Martin Zinkevich, Markus Weimer, Lihong Li and Alex Smola covering the research area of Artificial Intelligence and Statistics and Probability. It is primarily cited by scholars working on Artificial Intelligence (450 citations), Computer Vision and Pattern Recognition (224 citations) and Computer Networks and Communications (113 citations). Published in Neural Information Processing Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w8244415.