Sparse Gaussian Processes using Pseudo-inputs
- Authors
- Edward SnelsonZoubin Ghahramani
- Journal
- UCL Discovery (University College London)
In The Last Decade
doi.org/w5651000 →Countries where authors are citing Sparse Gaussian Processes using Pseudo-inputs
This map shows the geographic impact of Sparse Gaussian Processes using Pseudo-inputs. 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 Sparse Gaussian Processes using Pseudo-inputs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sparse Gaussian Processes using Pseudo-inputs more than expected).
Fields of papers citing Sparse Gaussian Processes using Pseudo-inputs
This network shows the impact of Sparse Gaussian Processes using Pseudo-inputs. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Sparse Gaussian Processes using Pseudo-inputs.
About Sparse Gaussian Processes using Pseudo-inputs
This paper, published in 2005, received 787 indexed citations . Written by Edward Snelson and Zoubin Ghahramani covering the research area of Control and Systems Engineering, Artificial Intelligence and Computational Theory and Mathematics. It is primarily cited by scholars working on Artificial Intelligence (562 citations), Control and Systems Engineering (235 citations) and Computational Theory and Mathematics (178 citations). Published in UCL Discovery (University College London).
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/w5651000.