Chinnadhurai Sankar
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Information Systems
- Signal Processing
- Co-authors
- Daniel DuckworthSemih YavuzBill ByrneArvind NeelakantanAmit DubeyBen GoodrichYoshua BengioSamira Ebrahimi Kahou
- Topics
- Topic Modeling (5 papers)Speech and dialogue systems (4 papers)Neural Networks and Applications (2 papers)
- Journals
- arXiv (Cornell University)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language TechnologiesProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesCanadaAlgeria
In The Last Decade
Chinnadhurai Sankar
9 papers receiving 143 citations
Peers
Comparison fields: 5 of 32
- Artificial Intelligence 129
- Computer Vision and Pattern Recognition 30
- Electrical and Electronic Engineering 10
- Information Systems 6
- Signal Processing 6
Countries citing papers authored by Chinnadhurai Sankar
This map shows the geographic impact of Chinnadhurai Sankar'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 Chinnadhurai Sankar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chinnadhurai Sankar more than expected).
Fields of papers citing papers by Chinnadhurai Sankar
This network shows the impact of papers produced by Chinnadhurai Sankar. 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 Chinnadhurai Sankar. The network helps show where Chinnadhurai Sankar may publish in the future.
Co-authorship network of co-authors of Chinnadhurai Sankar
This figure shows the co-authorship network connecting the top 25 collaborators of Chinnadhurai Sankar. A scholar is included among the top collaborators of Chinnadhurai Sankar 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 Chinnadhurai Sankar. Chinnadhurai Sankar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 9 | |
| 3 | 5 | |
| 4 | 6 | |
| 5 | 2 | |
| 6 | On the Robustness of Projection Neural Networks For Efficient Text Representation: An Empirical Study. | 2 |
| 7 | 92 | |
| 8 | 33 | |
| 9 | 5 |
About Chinnadhurai Sankar
Chinnadhurai Sankar is a scholar working on Artificial Intelligence, Hardware and Architecture and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 155 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Speech and dialogue systems (4 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (129 citations), Computer Vision and Pattern Recognition (30 citations) and Signal Processing (6 citations). Chinnadhurai Sankar has collaborated with scholars based in United States, Canada and Algeria. Frequent co-authors include Daniel Duckworth, Semih Yavuz, Bill Byrne, Arvind Neelakantan, Amit Dubey, Ben Goodrich, Yoshua Bengio, Samira Ebrahimi Kahou, Eugene Vorontsov and Andrew Thangaraj. Their work appears in journals such as arXiv (Cornell University), Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies and Proceedings of the AAAI Conference on Artificial Intelligence.
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.