Trapit Bansal
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Advanced Graph Neural Networks
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Information Systems top 10%
- Recommender Systems and Techniques
Papers in
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- Topic Modeling 9
- Natural Language Processing Techniques 4
- Domain Adaptation and Few-Shot Learning 2
- Advanced Graph Neural Networks 1
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- Biomedical Text Mining and Ontologies 2
- Co-authors
- Andrew McCallum (5 shared papers)Chiranjib Bhattacharyya (4 shared papers)Da-Cheng Juan (1 shared paper)Sujith Ravi (1 shared paper)Mrinal Kanti Das (3 shared papers)Patrick Verga (1 shared paper)Nathan Greenberg (1 shared paper)Ravindran Kannan (1 shared paper)
- Journals
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Trapit Bansal
10 papers receiving 214 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 200
- Information Systems 73
- Management Science and Operations Research 31
- Health Informatics 3
- Computer Vision and Pattern Recognition 27
Countries citing papers authored by Trapit Bansal
This map shows the geographic impact of Trapit Bansal'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 Trapit Bansal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Trapit Bansal more than expected).
Fields of papers citing papers by Trapit Bansal
This network shows the impact of papers produced by Trapit Bansal. 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 Trapit Bansal. The network helps show where Trapit Bansal may publish in the future.
Co-authors
The 12 scholars most cited alongside Trapit Bansal, 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 | 2019 | 91 | |
| 2 | 2015 | 54 | |
| 3 | 2018 | 37 | |
| 4 | 2021 | 15 | |
| 5 | 2014 | 12 | |
| 6 | 2014 | 7 | |
| 7 | 2020 | 5 | |
| 8 | 2022 | 2 | |
| 9 | 2021 | 1 | |
| 10 | 2015 | 1 |
About Trapit Bansal
Trapit Bansal is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Information Systems and Statistical and Nonlinear Physics, having authored 10 papers that have together received 225 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (4 papers), Multimodal Machine Learning Applications (2 papers), Biomedical Text Mining and Ontologies (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Tensor decomposition and applications (1 paper), Advanced Graph Neural Networks (1 paper) and Complex Network Analysis Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (200 citations), Information Systems (73 citations), Management Science and Operations Research (31 citations), Health Informatics (3 citations) and Computer Vision and Pattern Recognition (27 citations). Trapit Bansal has collaborated with scholars based in United States and India. Frequent co-authors include Andrew McCallum, Chiranjib Bhattacharyya, Da-Cheng Juan, Sujith Ravi, Mrinal Kanti Das, Patrick Verga, Nathan Greenberg, Ravindran Kannan, Tsendsuren Munkhdalai and Tong Wang. Their work appears in journals such as Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).
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.