Scott Gray
Impact in
- Computational Mathematics top 5%
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- Advanced Neural Network Applications
Papers in
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- Advanced Battery Technologies Research 1
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- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Andrew Lavin (1 shared paper)Thomas A. Everett (1 shared paper)Kee Sung Han (1 shared paper)Nav Nidhi Rajput (1 shared paper)Vijayakumar Murugesan (1 shared paper)Rasha Atwi (1 shared paper)Bharat Gwalani (1 shared paper)Vilas G. Pol (1 shared paper)
- Journals
- SAE technical papers on CD-ROM/SAE technical paper series (2 papers)Nature Communications (1 paper)Tourism Planning & Development (1 paper)Neural Information Processing Systems (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Scott Gray
9 papers receiving 906 citations
Scott Gray's Hit Papers
Peers
Comparison fields: 5 of 88
- Computational Mathematics 23
- Computer Vision and Pattern Recognition 480
- Hardware and Architecture 141
- Automotive Engineering 137
- Electrical and Electronic Engineering 456
Countries citing papers authored by Scott Gray
This map shows the geographic impact of Scott Gray'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 Scott Gray with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Gray more than expected).
Fields of papers citing papers by Scott Gray
This network shows the impact of papers produced by Scott Gray. 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 Scott Gray. The network helps show where Scott Gray may publish in the future.
Co-authors
The 25 scholars most cited alongside Scott Gray, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Fast Algorithms for Convolutional Neural Networks Hit paper breakdown → | 2016 | 566 |
| 2 | Non-polar ether-based electrolyte solutions for stable high-voltage non-aqueous lithium metal batteries Hit paper breakdown → | 2023 | 216 |
| 3 | Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks | 2017 | 77 |
| 4 | 2014 | 34 | |
| 5 | 1988 | 15 | |
| 6 | 1993 | 10 | |
| 7 | Zero-Shot Text-to-Image Generation | 2021 | 6 |
| 8 | 2020 | 5 | |
| 9 | 2023 | 1 |
About Scott Gray
Scott Gray is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering and Urban Studies, having authored 9 papers that have together received 930 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Battery Materials and Technologies (1 paper), Electricity Theft Detection Techniques (1 paper), Advanced Battery Technologies Research (1 paper), Adversarial Robustness in Machine Learning (1 paper), Structural Health Monitoring Techniques (1 paper), Entrepreneurship Studies and Influences (1 paper) and Advancements in Battery Materials (1 paper). The work is most often cited by research in Computational Mathematics (23 citations), Computer Vision and Pattern Recognition (480 citations), Hardware and Architecture (141 citations), Automotive Engineering (137 citations) and Electrical and Electronic Engineering (456 citations). Scott Gray has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Andrew Lavin, Thomas A. Everett, Kee Sung Han, Nav Nidhi Rajput, Vijayakumar Murugesan, Rasha Atwi, Bharat Gwalani, Vilas G. Pol, Bhuvaneswari M. Sivakumar and Derek H. Alderman. Their work appears in journals such as SAE technical papers on CD-ROM/SAE technical paper series, Nature Communications, Tourism Planning & Development, Neural Information Processing Systems 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.