Vidur Joshi
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
- Advanced Text Analysis Techniques
- Text Readability and Simplification
- Domain Adaptation and Few-Shot Learning
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- Multimodal Machine Learning Applications
Papers in
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- Natural Language Processing Techniques 3
- Topic Modeling 3
- Semantic Web and Ontologies 1
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- Cardiac, Anesthesia and Surgical Outcomes 1
- Co-authors
- Robert Logan (1 shared paper)Mark E Neumann (1 shared paper)Noah A. Smith (1 shared paper)Roy Schwartz (1 shared paper)Sameer Singh (1 shared paper)Matthew E. Peters (1 shared paper)Ronan Le Bras (1 shared paper)Mark Hopkins (2 shared papers)
- Journals
- Journal of Neuroanaesthesiology and Critical Care (1 paper)
- Partner nations
- United States
In The Last Decade
Vidur Joshi
3 papers receiving 363 citations
Vidur Joshi's Hit Papers
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 348
- Computer Vision and Pattern Recognition 80
- Management Science and Operations Research 20
- Health Informatics 2
- Information Systems 22
Countries citing papers authored by Vidur Joshi
This map shows the geographic impact of Vidur Joshi'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 Vidur Joshi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vidur Joshi more than expected).
Fields of papers citing papers by Vidur Joshi
This network shows the impact of papers produced by Vidur Joshi. 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 Vidur Joshi. The network helps show where Vidur Joshi may publish in the future.
Co-authors
The 10 scholars most cited alongside Vidur Joshi, 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 | Knowledge Enhanced Contextual Word Representations Hit paper breakdown → | 2019 | 368 |
| 2 | 2017 | 7 | |
| 3 | 2017 | 1 | |
| 4 | 2024 | 0 |
About Vidur Joshi
Vidur Joshi is a scholar working on Artificial Intelligence, Cardiology and Cardiovascular Medicine, Information Systems, Computer Vision and Pattern Recognition and Health Informatics, having authored 4 papers that have together received 376 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Topic Modeling (3 papers), Artificial Intelligence in Healthcare and Education (1 paper), Cardiac, Anesthesia and Surgical Outcomes (1 paper), Software Engineering Research (1 paper), Medical Imaging and Analysis (1 paper), Semantic Web and Ontologies (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (348 citations), Computer Vision and Pattern Recognition (80 citations), Management Science and Operations Research (20 citations), Health Informatics (2 citations) and Information Systems (22 citations). Vidur Joshi has collaborated with scholars based in United States. Frequent co-authors include Robert Logan, Mark E Neumann, Noah A. Smith, Roy Schwartz, Sameer Singh, Matthew E. Peters, Ronan Le Bras, Mark Hopkins, Zhenrui Liao and Shailendra Joshi. Their work appears in journals such as Journal of Neuroanaesthesiology and Critical Care.
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.