Suyog Gupta
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Hardware and Architecture top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Ankur AgrawalKailash GopalakrishnanPritish NarayananFei WangWei ZhangJi LiuXiangru LianBerkin Akin
- Topics
- Advanced Neural Network Applications (5 papers)Stochastic Gradient Optimization Techniques (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)
- Journals
- International Joint Conference on Artificial IntelligenceInternational Conference on Machine Learning
- Partner nations
- United States
In The Last Decade
Suyog Gupta
8 papers receiving 780 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 456
- Artificial Intelligence 426
- Electrical and Electronic Engineering 270
- Hardware and Architecture 110
- Computer Networks and Communications 106
Countries citing papers authored by Suyog Gupta
This map shows the geographic impact of Suyog Gupta'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 Suyog Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suyog Gupta more than expected).
Fields of papers citing papers by Suyog Gupta
This network shows the impact of papers produced by Suyog Gupta. 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 Suyog Gupta. The network helps show where Suyog Gupta may publish in the future.
Co-authorship network of co-authors of Suyog Gupta
This figure shows the co-authorship network connecting the top 25 collaborators of Suyog Gupta. A scholar is included among the top collaborators of Suyog Gupta 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 Suyog Gupta. Suyog Gupta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 97 | |
| 2 | 7 | |
| 3 | 27 | |
| 4 | 20 | |
| 5 | Staleness-aware async-SGD for distributed deep learning | 100 |
| 6 | 67 | |
| 7 | 82 | |
| 8 | Deep Learning with Limited Numerical Precisionbreakdown → | 435 |
About Suyog Gupta
Suyog Gupta is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 8 papers that have together received 835 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (5 papers), Stochastic Gradient Optimization Techniques (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (456 citations), Computational Mathematics (11 citations) and Hardware and Architecture (110 citations). Suyog Gupta has collaborated with scholars based in United States. Frequent co-authors include Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan, Fei Wang, Wei Zhang, Wei Zhang, Ji Liu, Xiangru Lian, Berkin Akin and Yongzhe Wang. Their work appears in journals such as International Joint Conference on Artificial Intelligence and International Conference on Machine Learning.
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.