Hamming Distance Metric Learning
Impact in
Classified as
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w9626109 →Countries where authors are citing Hamming Distance Metric Learning
This map shows the geographic impact of Hamming Distance Metric 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 Hamming Distance Metric Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hamming Distance Metric Learning more than expected).
Fields of papers citing Hamming Distance Metric Learning
This network shows the impact of Hamming Distance Metric Learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hamming Distance Metric Learning.
About Hamming Distance Metric Learning
This paper, published in 2012, received 322 indexed citations . Written by Mohammad Norouzi, David J. Fleet and Ruslan Salakhutdinov covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (249 citations), Artificial Intelligence (92 citations), Signal Processing (23 citations), Aerospace Engineering (19 citations) and Computer Networks and Communications (17 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/w9626109.