Deep learning with COTS HPC systems
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w9514796 →Countries where authors are citing Deep learning with COTS HPC systems
This map shows the geographic impact of Deep learning with COTS HPC systems. 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 Deep learning with COTS HPC systems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep learning with COTS HPC systems more than expected).
Fields of papers citing Deep learning with COTS HPC systems
This network shows the impact of Deep learning with COTS HPC systems. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep learning with COTS HPC systems.
About Deep learning with COTS HPC systems
This paper, published in 2013, received 370 indexed citations . Written by Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro and Andrew Ng covering the research area of Hardware and Architecture and Electrical and Electronic Engineering. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (207 citations), Artificial Intelligence (188 citations) and Electrical and Electronic Engineering (98 citations). Published in International Conference on Machine Learning.
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/w9514796.