Rohit Babbar
Impact in
- Artificial Intelligence top 5%
- Text and Document Classification Technologies
- Topic Modeling
- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Sentiment Analysis and Opinion Mining
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- Advanced Neural Network Applications
Papers in
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- Text and Document Classification Technologies 13
- Machine Learning and Data Classification 6
- Machine Learning and Algorithms 5
- Topic Modeling 4
- Domain Adaptation and Few-Shot Learning 3
- Sentiment Analysis and Opinion Mining 2
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- Web Data Mining and Analysis 3
- Co-authors
- Bernhard Schölkopf (4 shared papers)Carlee Joe‐Wong (1 shared paper)Mario Di Francesco (1 shared paper)Han Xiao (1 shared paper)Nidhi Singh (1 shared paper)Krzysztof Dembczyński (2 shared papers)Marek Wydmuch (2 shared papers)Massih-Reza Amini (1 shared paper)
- Journals
- Machine Learning (3 papers)Frontiers in Endocrinology (1 paper)MPG.PuRe (Max Planck Society) (1 paper)arXiv (Cornell University) (2 papers)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- FinlandUnited StatesGermany
In The Last Decade
Rohit Babbar
19 papers receiving 499 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 353
- Computer Vision and Pattern Recognition 108
- Information Systems 108
- Computer Networks and Communications 109
- Neurology 15
Countries citing papers authored by Rohit Babbar
This map shows the geographic impact of Rohit Babbar'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 Rohit Babbar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rohit Babbar more than expected).
Fields of papers citing papers by Rohit Babbar
This network shows the impact of papers produced by Rohit Babbar. 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 Rohit Babbar. The network helps show where Rohit Babbar may publish in the future.
Co-authors
The 20 scholars most cited alongside Rohit Babbar, 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 | 2020 | 143 | |
| 2 | 2017 | 102 | |
| 3 | 2019 | 79 | |
| 4 | 2019 | 67 | |
| 5 | 2010 | 21 | |
| 6 | 2021 | 17 | |
| 7 | 2018 | 15 | |
| 8 | 2022 | 14 | |
| 9 | 2014 | 9 | |
| 10 | 2022 | 9 | |
| 11 | 2022 | 8 | |
| 12 | 2021 | 5 | |
| 13 | 2022 | 4 | |
| 14 | 2023 | 4 | |
| 15 | 2023 | 3 | |
| 16 | 2016 | 3 | |
| 17 | 2024 | 3 | |
| 18 | A Simple and Effective Scheme for Data Pre-processing in Extreme Classification | 2019 | 1 |
| 19 | 2025 | 1 | |
| 20 | 2025 | 0 |
About Rohit Babbar
Rohit Babbar is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 20 papers that have together received 508 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (13 papers), Machine Learning and Data Classification (6 papers), Machine Learning and Algorithms (5 papers), Topic Modeling (4 papers), Web Data Mining and Analysis (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Sentiment Analysis and Opinion Mining (2 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Artificial Intelligence (353 citations), Computer Vision and Pattern Recognition (108 citations), Information Systems (108 citations), Computer Networks and Communications (109 citations) and Neurology (15 citations). Rohit Babbar has collaborated with scholars based in Finland, United States and Germany. Frequent co-authors include Bernhard Schölkopf, Carlee Joe‐Wong, Mario Di Francesco, Han Xiao, Nidhi Singh, Krzysztof Dembczyński, Marek Wydmuch, Massih-Reza Amini, Róbert Wágner and Ioannis Partalas. Their work appears in journals such as Machine Learning, Frontiers in Endocrinology, MPG.PuRe (Max Planck Society), arXiv (Cornell University) and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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.