Vaibhav Kumar
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition
- Management Information Systems
- Management Science and Operations Research
- Co-authors
- Lei MoVasudeva VarmaDhruv KhattarJamie CallanMrinal Kanti DharManish ShrivastavaManish GuptaAlan W. Black
- Topics
- Topic Modeling (13 papers)Natural Language Processing Techniques (9 papers)Recommender Systems and Techniques (7 papers)
- Journals
- Journal of Molecular LiquidsIndian Journal of Science and TechnologyENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)
- Partner nations
- IndiaUnited StatesUnited Kingdom
In The Last Decade
Vaibhav Kumar
27 papers receiving 324 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 240
- Information Systems 130
- Computer Vision and Pattern Recognition 55
- Management Information Systems 30
- Management Science and Operations Research 21
Countries citing papers authored by Vaibhav Kumar
This map shows the geographic impact of Vaibhav Kumar'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 Vaibhav Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vaibhav Kumar more than expected).
Fields of papers citing papers by Vaibhav Kumar
This network shows the impact of papers produced by Vaibhav Kumar. 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 Vaibhav Kumar. The network helps show where Vaibhav Kumar may publish in the future.
Co-authorship network of co-authors of Vaibhav Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Vaibhav Kumar. A scholar is included among the top collaborators of Vaibhav Kumar 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 Vaibhav Kumar. Vaibhav Kumar 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 | 1 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 42 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 22 | |
| 11 | 5 | |
| 12 | 6 | |
| 13 | 1 | |
| 14 | Enabling Code-Mixed Translation: Parallel Corpus Creation and MT Augmentation Approach | 30 |
| 15 | 76 | |
| 16 | Neural Content-Collaborative Filtering for News Recommendation. | 6 |
| 17 | 4 | |
| 18 | 15 | |
| 19 | Deep Neural Architecture for News Recommendation. | 18 |
| 20 | 2 |
About Vaibhav Kumar
Vaibhav Kumar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 34 papers that have together received 361 indexed citations. Recurring topics across this work include Topic Modeling (13 papers), Natural Language Processing Techniques (9 papers) and Recommender Systems and Techniques (7 papers). The work is most often cited by research in Artificial Intelligence (240 citations), Information Systems (130 citations) and Management Information Systems (30 citations). Vaibhav Kumar has collaborated with scholars based in India, United States and United Kingdom. Frequent co-authors include Lei Mo, Vasudeva Varma, Dhruv Khattar, Jamie Callan, Mrinal Kanti Dhar, Manish Shrivastava, Manish Gupta, Alan W. Black, Chenyan Xiong and Jeff Dalton. Their work appears in journals such as Journal of Molecular Liquids, Indian Journal of Science and Technology and ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam).
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.