Jeffrey Ling
Impact in
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- Handwritten Text Recognition Techniques
- Multimodal Machine Learning Applications
- Image Processing and 3D Reconstruction
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- Natural Language Processing Techniques
- Topic Modeling
Papers in
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- Natural Language Processing Techniques 3
- Topic Modeling 2
- Advanced Text Analysis Techniques 1
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- Social Media and Politics 1
- Co-authors
- Alexander M. Rush (2 shared papers)Yuntian Deng (1 shared paper)Anssi Kanervisto (1 shared paper)Tom W. Rice (2 shared papers)Balakrishnan Varadarajan (1 shared paper)Paul S. Covington (1 shared paper)Haoyu Chen (1 shared paper)Tom Kwiatkowski (1 shared paper)
- Journals
- Space and Polity (1 paper)International Journal of Technology Assessment in Health Care (1 paper)BMC Research Notes (1 paper)2022 International Conference on Robotics and Automation (ICRA) (1 paper)Women & Politics (1 paper)
- Partner nations
- United StatesIsraelFinland
In The Last Decade
Jeffrey Ling
7 papers receiving 81 citations
Peers
Comparison fields: 5 of 35
- Computer Vision and Pattern Recognition 50
- Artificial Intelligence 49
- Computational Theory and Mathematics 15
- Media Technology 8
- Automotive Engineering 8
Countries citing papers authored by Jeffrey Ling
This map shows the geographic impact of Jeffrey Ling's research. 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 Jeffrey Ling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey Ling more than expected).
Fields of papers citing papers by Jeffrey Ling
This network shows the impact of papers produced by Jeffrey Ling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jeffrey Ling. The network helps show where Jeffrey Ling may publish in the future.
Co-authors
The 19 scholars most cited alongside Jeffrey Ling, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Image-to-Markup Generation with Coarse-to-Fine Attention | 2017 | 42 |
| 2 | 2017 | 20 | |
| 3 | 2022 | 12 | |
| 4 | 2002 | 9 | |
| 5 | 2013 | 3 | |
| 6 | 1999 | 2 | |
| 7 | Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories | 2019 | 2 |
| 8 | 2022 | 1 |
About Jeffrey Ling
Jeffrey Ling is a scholar working on Artificial Intelligence, Communication, Molecular Biology, Political Science and International Relations and Automotive Engineering, having authored 8 papers that have together received 91 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Topic Modeling (2 papers), Advanced Text Analysis Techniques (1 paper), Social Media and Politics (1 paper), Social and Cultural Dynamics (1 paper), Handwritten Text Recognition Techniques (1 paper), Health Systems, Economic Evaluations, Quality of Life (1 paper) and Advanced Proteomics Techniques and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (50 citations), Artificial Intelligence (49 citations), Computational Theory and Mathematics (15 citations), Media Technology (8 citations) and Automotive Engineering (8 citations). Jeffrey Ling has collaborated with scholars based in United States, Israel and Finland. Frequent co-authors include Alexander M. Rush, Yuntian Deng, Anssi Kanervisto, Tom W. Rice, Balakrishnan Varadarajan, Paul S. Covington, Haoyu Chen, Tom Kwiatkowski, Qiaojun Wen and Jun Ji. Their work appears in journals such as Space and Polity, International Journal of Technology Assessment in Health Care, BMC Research Notes, 2022 International Conference on Robotics and Automation (ICRA) and Women & Politics.
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.