Efficient and robust automated machine learning
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w8943496 →Countries where authors are citing Efficient and robust automated machine learning
This map shows the geographic impact of Efficient and robust automated machine learning. 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 Efficient and robust automated machine learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Efficient and robust automated machine learning more than expected).
Fields of papers citing Efficient and robust automated machine learning
This network shows the impact of Efficient and robust automated machine learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Efficient and robust automated machine learning.
About Efficient and robust automated machine learning
This paper, published in 2015, received 704 indexed citations . Written by Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum and Frank Hutter covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (440 citations), Computer Vision and Pattern Recognition (77 citations) and Information Systems (70 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/w8943496.