Saurabh Tiwary
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Sociology and Political Science
- Management Science and Operations Research
- Co-authors
- Song XiaRangan MajumderJianfeng GaoLi DengTri Gia NguyenChenyan XiongPaul N. BennettNick Craswell
- Topics
- Topic Modeling (7 papers)Natural Language Processing Techniques (6 papers)Multimodal Machine Learning Applications (2 papers)
- Journals
- Neural Information Processing SystemsInternational Conference on Learning Representations
- Partner nations
- United StatesUnited KingdomFinland
In The Last Decade
Saurabh Tiwary
9 papers receiving 306 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 308
- Computer Vision and Pattern Recognition 104
- Information Systems 82
- Sociology and Political Science 14
- Management Science and Operations Research 13
Countries citing papers authored by Saurabh Tiwary
This map shows the geographic impact of Saurabh Tiwary'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 Saurabh Tiwary with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saurabh Tiwary more than expected).
Fields of papers citing papers by Saurabh Tiwary
This network shows the impact of papers produced by Saurabh Tiwary. 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 Saurabh Tiwary. The network helps show where Saurabh Tiwary may publish in the future.
Co-authorship network of co-authors of Saurabh Tiwary
This figure shows the co-authorship network connecting the top 25 collaborators of Saurabh Tiwary. A scholar is included among the top collaborators of Saurabh Tiwary 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 Saurabh Tiwary. Saurabh Tiwary 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 | 2 | |
| 3 | 3 | |
| 4 | Pretrain Knowledge-Aware Language Models | 3 |
| 5 | Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention | 62 |
| 6 | 46 | |
| 7 | 1 | |
| 8 | 41 | |
| 9 | MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. | 166 |
About Saurabh Tiwary
Saurabh Tiwary is a scholar working on Health Informatics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 327 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Artificial Intelligence (308 citations), Computer Vision and Pattern Recognition (104 citations) and Information Systems (82 citations). Saurabh Tiwary has collaborated with scholars based in United States, United Kingdom and Finland. Frequent co-authors include Song Xia, Rangan Majumder, Jianfeng Gao, Li Deng, Tri Gia Nguyen, Chenyan Xiong, Paul N. Bennett, Nick Craswell, Corby Rosset and Chen Zhao. Their work appears in journals such as Neural Information Processing Systems and International Conference on Learning Representations.
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.