Ameya Prabhu
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Information Systems
- Electrical and Electronic Engineering
- Co-authors
- Manish ShrivastavaAditya JoshiVasudeva VarmaManeesh SinghBernard GhanemPhilip H. S. TorrHasan Abed Al Kader HammoudAdel Bibi
- Topics
- Domain Adaptation and Few-Shot Learning (3 papers)Machine Learning and Algorithms (2 papers)Topic Modeling (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputer Science Applications
- Journals
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)arXiv (Cornell University)International Conference on Computational Linguistics
- Partner nations
- United KingdomIndiaSaudi Arabia
In The Last Decade
Ameya Prabhu
9 papers receiving 126 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 115
- Computer Vision and Pattern Recognition 31
- Computer Networks and Communications 10
- Information Systems 10
- Electrical and Electronic Engineering 7
Countries citing papers authored by Ameya Prabhu
This map shows the geographic impact of Ameya Prabhu'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 Ameya Prabhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ameya Prabhu more than expected).
Fields of papers citing papers by Ameya Prabhu
This network shows the impact of papers produced by Ameya Prabhu. 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 Ameya Prabhu. The network helps show where Ameya Prabhu may publish in the future.
Co-authorship network of co-authors of Ameya Prabhu
This figure shows the co-authorship network connecting the top 25 collaborators of Ameya Prabhu. A scholar is included among the top collaborators of Ameya Prabhu 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 Ameya Prabhu. Ameya Prabhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 21 | |
| 4 | 5 | |
| 5 | 20 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 29 | |
| 9 | Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity. | 3 |
| 10 | Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text | 49 |
| 11 | 1 |
About Ameya Prabhu
Ameya Prabhu is a scholar working on Artificial Intelligence, Hardware and Architecture and Computational Theory and Mathematics, having authored 11 papers that have together received 133 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (3 papers), Machine Learning and Algorithms (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Artificial Intelligence (115 citations), Computer Vision and Pattern Recognition (31 citations) and Computer Science Applications (5 citations). Ameya Prabhu has collaborated with scholars based in United Kingdom, India and Saudi Arabia. Frequent co-authors include Manish Shrivastava, Aditya Joshi, Vasudeva Varma, Maneesh Singh, Bernard Ghanem, Philip H. S. Torr, Hasan Abed Al Kader Hammoud, Adel Bibi, Ser-Nam Lim and Puneet K. Dokania. Their work appears in journals such as King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology), arXiv (Cornell University) and International Conference on 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.