Peter Jin
Impact in
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- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Advanced Image and Video Retrieval Techniques
- Advanced Image Processing Techniques
- Video Surveillance and Tracking Methods
Papers in
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- Machine Learning and ELM 2
- Stochastic Gradient Optimization Techniques 2
- Adversarial Robustness in Machine Learning 1
- Reinforcement Learning in Robotics 1
- Privacy-Preserving Technologies in Data 1
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- Advanced Neural Network Applications 3
- Handwritten Text Recognition Techniques 1
- Advanced Image and Video Retrieval Techniques 1
- Co-authors
- Kurt Keutzer (5 shared papers)Bichen Wu (1 shared paper)Joseph E. Gonzalez (1 shared paper)Alvin Wan (1 shared paper)Amir Gholaminejad (1 shared paper)Sicheng Zhao (1 shared paper)Xiangyu Yue (1 shared paper)Amir Gholami (1 shared paper)
- Journals
- Society for Industrial and Applied Mathematics eBooks (2 papers)International Conference on Learning Representations (2 papers)arXiv (Cornell University) (1 paper)eScholarship (California Digital Library) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Peter Jin
6 papers receiving 318 citations
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 247
- Computational Mathematics 4
- Artificial Intelligence 147
- Media Technology 31
- Signal Processing 24
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 | 244 | |
| 2 | 2018 | 46 | |
| 3 | 2016 | 32 | |
| 4 | Spatially Parallel Convolutions. | 2018 | 3 |
| 5 | 2025 | 2 | |
| 6 | Regret Minimization for Partially Observable Deep Reinforcement Learning | 2018 | 1 |
| 7 | 2026 | 0 |
About Peter Jin
Peter Jin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics and Computational Theory and Mathematics, having authored 7 papers that have together received 328 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Machine Learning and ELM (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Adversarial Robustness in Machine Learning (1 paper), Reinforcement Learning in Robotics (1 paper), Privacy-Preserving Technologies in Data (1 paper), Handwritten Text Recognition Techniques (1 paper) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (247 citations), Computational Mathematics (4 citations), Artificial Intelligence (147 citations), Media Technology (31 citations) and Signal Processing (24 citations). Peter Jin has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Kurt Keutzer, Bichen Wu, Joseph E. Gonzalez, Alvin Wan, Amir Gholaminejad, Sicheng Zhao, Xiangyu Yue, Amir Gholami, Ariful Azad and Aydın Buluç. Their work appears in journals such as Society for Industrial and Applied Mathematics eBooks, International Conference on Learning Representations, arXiv (Cornell University) and eScholarship (California Digital Library).
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.