Saksham Singhal
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing
- Information Systems
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Furu WeiWenhui WangLi DongSong XiaZewen ChiHangbo BaoDong LiZhiliang Peng
- Topics
- Topic Modeling (7 papers)Natural Language Processing Techniques (6 papers)Speech Recognition and Synthesis (4 papers)
- Journals
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- ChinaUnited StatesIndia
In The Last Decade
Saksham Singhal
10 papers receiving 490 citations
Hit Papers
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 368
- Computer Vision and Pattern Recognition 289
- Signal Processing 25
- Information Systems 20
- Radiology, Nuclear Medicine and Imaging 15
Countries citing papers authored by Saksham Singhal
This map shows the geographic impact of Saksham Singhal'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 Saksham Singhal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saksham Singhal more than expected).
Fields of papers citing papers by Saksham Singhal
This network shows the impact of papers produced by Saksham Singhal. 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 Saksham Singhal. The network helps show where Saksham Singhal may publish in the future.
Co-authorship network of co-authors of Saksham Singhal
This figure shows the co-authorship network connecting the top 25 collaborators of Saksham Singhal. A scholar is included among the top collaborators of Saksham Singhal 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 Saksham Singhal. Saksham Singhal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasksbreakdown → | 247 |
| 5 | 40 | |
| 6 | 143 | |
| 7 | 36 | |
| 8 | 20 | |
| 9 | 12 | |
| 10 | 1 |
About Saksham Singhal
Saksham Singhal is a scholar working on General Social Sciences, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 507 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers) and Speech Recognition and Synthesis (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (289 citations), Artificial Intelligence (368 citations) and Signal Processing (25 citations). Saksham Singhal has collaborated with scholars based in China, United States and India. Frequent co-authors include Furu Wei, Wenhui Wang, Li Dong, Song Xia, Zewen Chi, Hangbo Bao, Dong Li, Zhiliang Peng, Xian-Ling Mao and Kriti Aggarwal. Their work appears in journals such as Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.