Peter Jin
Impact in
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- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Face recognition and analysis
- Advanced Image Processing Techniques
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
- Embedded Systems Design Techniques
Papers in
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- Advanced Neural Network Applications 3
- Medical Image Segmentation Techniques 1
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- Parallel Computing and Optimization Techniques 2
- Co-authors
- Kurt Keutzer (5 shared papers)Xiangyu Yue (3 shared papers)Sicheng Zhao (3 shared papers)Bichen Wu (2 shared papers)Amir Gholami (2 shared papers)Amir Gholaminejad (2 shared papers)Kiseok Kwon (1 shared paper)Alvin Wan (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)arXiv (Cornell University) (1 paper)IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (1 paper)International Conference on Learning Representations (1 paper)IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Peter Jin
5 papers receiving 593 citations
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 450
- Hardware and Architecture 63
- Artificial Intelligence 68
- Neurology 14
- Signal Processing 10
Countries citing papers authored by Peter Jin
This map shows the geographic impact of Peter Jin'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 Peter Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Jin more than expected).
Fields of papers citing papers by Peter Jin
This network shows the impact of papers produced by Peter Jin. 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 Peter Jin. The network helps show where Peter Jin may publish in the future.
Co-authors
The 14 scholars most cited alongside Peter Jin, 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 | 2018 | 275 | |
| 2 | 2018 | 254 | |
| 3 | 2018 | 53 | |
| 4 | 2019 | 34 | |
| 5 | Spatially Parallel Convolutions. | 2018 | 4 |
About Peter Jin
Peter Jin is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture, Artificial Intelligence, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 5 papers that have together received 620 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Parallel Computing and Optimization Techniques (2 papers), Medical Image Segmentation Techniques (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (450 citations), Hardware and Architecture (63 citations), Artificial Intelligence (68 citations), Neurology (14 citations) and Signal Processing (10 citations). Peter Jin has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Kurt Keutzer, Xiangyu Yue, Sicheng Zhao, Bichen Wu, Amir Gholami, Amir Gholaminejad, Kiseok Kwon, Alvin Wan, Joseph E. Gonzalez and Aydın Buluç. Their work appears in journals such as Lecture notes in computer science, arXiv (Cornell University), IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, International Conference on Learning Representations and IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.