Large-scale Video Classification with Convolutional Neural Networks
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doi.org/w38201820 →Countries where authors are citing Large-scale Video Classification with Convolutional Neural Networks
This map shows the geographic impact of Large-scale Video Classification with Convolutional Neural Networks. 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 Large-scale Video Classification with Convolutional Neural Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Large-scale Video Classification with Convolutional Neural Networks more than expected).
Fields of papers citing Large-scale Video Classification with Convolutional Neural Networks
This network shows the impact of Large-scale Video Classification with Convolutional Neural Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Large-scale Video Classification with Convolutional Neural Networks.
About Large-scale Video Classification with Convolutional Neural Networks
This paper, published in 2014, received 578 indexed citations . Written by Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar and Li Fei-Fei covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (482 citations), Artificial Intelligence (236 citations), Biomedical Engineering (99 citations), Human-Computer Interaction (63 citations) and Signal Processing (27 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/w38201820.