Sharan Narang
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- Domain Adaptation and Few-Shot Learning
- Text Readability and Simplification
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
Papers in
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- Natural Language Processing Techniques 5
- Topic Modeling 4
- Neural Networks and Applications 1
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- Multimodal Machine Learning Applications 3
- Human Pose and Action Recognition 1
- Advanced Neural Network Applications 1
- Co-authors
- Colin Raffel (2 shared papers)Adam P. Roberts (2 shared papers)Noah Constant (2 shared papers)Linting Xue (1 shared paper)Mihir Kale (1 shared paper)Rami Al‐Rfou (1 shared paper)Aditya Barua (1 shared paper)Erich Elsen (2 shared papers)
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)arXiv (Cornell University) (2 papers)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Sharan Narang
7 papers receiving 248 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 192
- Computer Vision and Pattern Recognition 104
- Computational Mathematics 2
- Hardware and Architecture 8
- Signal Processing 11
Countries citing papers authored by Sharan Narang
This map shows the geographic impact of Sharan Narang'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 Sharan Narang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sharan Narang more than expected).
Fields of papers citing papers by Sharan Narang
This network shows the impact of papers produced by Sharan Narang. 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 Sharan Narang. The network helps show where Sharan Narang may publish in the future.
Co-authors
The 25 scholars most cited alongside Sharan Narang, 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 | 2022 | 114 | |
| 2 | Mixed Precision Training | 2017 | 62 |
| 3 | 2021 | 37 | |
| 4 | 2023 | 18 | |
| 5 | 2023 | 15 | |
| 6 | DSD: Dense-Sparse-Dense Training for Deep Neural Networks | 2016 | 12 |
| 7 | 2023 | 11 |
About Sharan Narang
Sharan Narang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Theory and Mathematics and Infectious Diseases, having authored 7 papers that have together received 269 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers), Multimodal Machine Learning Applications (3 papers), Numerical Methods and Algorithms (1 paper), Neural Networks and Applications (1 paper), Model Reduction and Neural Networks (1 paper), Human Pose and Action Recognition (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Artificial Intelligence (192 citations), Computer Vision and Pattern Recognition (104 citations), Computational Mathematics (2 citations), Hardware and Architecture (8 citations) and Signal Processing (11 citations). Sharan Narang has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Colin Raffel, Adam P. Roberts, Noah Constant, Linting Xue, Mihir Kale, Rami Al‐Rfou, Aditya Barua, Erich Elsen, David Escudero García and Boris Ginsburg. Their work appears in journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.