Dimensionality Reduction by Learning an Invariant Mapping
- Journal
- SPIRE - Sciences Po Institutional REpository
In The Last Decade
doi.org/10.1109/cvpr.2006.100 →Countries where authors are citing Dimensionality Reduction by Learning an Invariant Mapping
This map shows the geographic impact of Dimensionality Reduction by Learning an Invariant Mapping. 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 Dimensionality Reduction by Learning an Invariant Mapping with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dimensionality Reduction by Learning an Invariant Mapping more than expected).
Fields of papers citing Dimensionality Reduction by Learning an Invariant Mapping
This network shows the impact of Dimensionality Reduction by Learning an Invariant Mapping. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Dimensionality Reduction by Learning an Invariant Mapping.
About Dimensionality Reduction by Learning an Invariant Mapping
This paper, published in 2006, received 2.9k indexed citations . Written by Raia Hadsell, Sumit Chopra and Yann LeCun covering the research area of Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (1.8k citations), Artificial Intelligence (1.4k citations) and Signal Processing (307 citations). Published in SPIRE - Sciences Po Institutional REpository.
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/10.1109/cvpr.2006.100.