Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
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doi.org/w29283818 →Countries where authors are citing Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
This map shows the geographic impact of Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). 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 Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) more than expected).
Fields of papers citing Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
This network shows the impact of Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010).
About Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
This paper, published in 2010, received 555 indexed citations . Written by Ryan P. Adams, George E. Dahl and Iain Murray. It is primarily cited by scholars working on Artificial Intelligence (344 citations), Information Systems (185 citations), Management Science and Operations Research (94 citations), Computer Vision and Pattern Recognition (71 citations) and Computer Networks and Communications (60 citations).
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/w29283818.