Vishrav Chaudhary
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Molecular Biology
- Signal Processing
- Co-authors
- Naman GoyalPeng‐Jen ChenFrancisco GuzmánAngela FanPhilipp KoehnGuillaume WenzekFrancisco GuzmánAlexis Conneau
- Topics
- Natural Language Processing Techniques (27 papers)Topic Modeling (24 papers)Text Readability and Simplification (7 papers)
- Journals
- Journal of Machine Learning ResearchLanguage Resources and EvaluationTransactions of the Association for Computational Linguistics
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Vishrav Chaudhary
26 papers receiving 552 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 569
- Computer Vision and Pattern Recognition 163
- Information Systems 45
- Molecular Biology 20
- Signal Processing 19
Countries citing papers authored by Vishrav Chaudhary
This map shows the geographic impact of Vishrav Chaudhary'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 Vishrav Chaudhary with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vishrav Chaudhary more than expected).
Fields of papers citing papers by Vishrav Chaudhary
This network shows the impact of papers produced by Vishrav Chaudhary. 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 Vishrav Chaudhary. The network helps show where Vishrav Chaudhary may publish in the future.
Co-authorship network of co-authors of Vishrav Chaudhary
This figure shows the co-authorship network connecting the top 25 collaborators of Vishrav Chaudhary. A scholar is included among the top collaborators of Vishrav Chaudhary based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vishrav Chaudhary. Vishrav Chaudhary is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 26 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 6 | |
| 9 | 0 | |
| 10 | 7 | |
| 11 | Beyond English-Centric Multilingual Machine Translation | 11 |
| 12 | 75 | |
| 13 | 14 | |
| 14 | 9 | |
| 15 | 29 | |
| 16 | 8 | |
| 17 | 4 | |
| 18 | 44 | |
| 19 | CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data | 41 |
| 20 | 41 |
About Vishrav Chaudhary
Vishrav Chaudhary is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 29 papers that have together received 603 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (27 papers), Topic Modeling (24 papers) and Text Readability and Simplification (7 papers). The work is most often cited by research in Artificial Intelligence (569 citations), Computer Vision and Pattern Recognition (163 citations) and Health Informatics (6 citations). Vishrav Chaudhary has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Naman Goyal, Peng‐Jen Chen, Francisco Guzmán, Angela Fan, Philipp Koehn, Guillaume Wenzek, Francisco Guzmán, Alexis Conneau, Édouard Grave and Ahmed El-Kishky. Their work appears in journals such as Journal of Machine Learning Research, Language Resources and Evaluation and Transactions of the Association for Computational Linguistics.
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.