Jiaming Song
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Human Pose and Action Recognition
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Machine Learning and Data Classification
- Adversarial Robustness in Machine Learning
Papers in
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- Generative Adversarial Networks and Image Synthesis 3
- Human Pose and Action Recognition 2
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- Anomaly Detection Techniques and Applications 2
- Explainable Artificial Intelligence (XAI) 2
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Stefano Ermon (7 shared papers)Shengjia Zhao (4 shared papers)Russell J. Stewart (2 shared papers)Volodymyr Kuleshov (2 shared papers)Hongyu Ren (2 shared papers)Stefano Ermon (1 shared paper)Mykel J. Kochenderfer (1 shared paper)Bahjat Kawar (1 shared paper)
- Journals
- Tsinghua Science & Technology (1 paper)AI Magazine (1 paper)Signal Processing (1 paper)IET conference proceedings. (1 paper)Uncertainty in Artificial Intelligence (1 paper)
- Partner nations
- ChinaUnited StatesItaly
In The Last Decade
Jiaming Song
12 papers receiving 205 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 95
- Artificial Intelligence 117
- Computational Mathematics 2
- Signal Processing 24
- Computer Graphics and Computer-Aided Design 6
Countries citing papers authored by Jiaming Song
This map shows the geographic impact of Jiaming Song'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 Jiaming Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiaming Song more than expected).
Fields of papers citing papers by Jiaming Song
This network shows the impact of papers produced by Jiaming Song. 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 Jiaming Song. The network helps show where Jiaming Song may publish in the future.
Co-authors
The 25 scholars most cited alongside Jiaming Song, 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 | 2019 | 135 | |
| 2 | Learning Hierarchical Features from Deep Generative Models | 2017 | 41 |
| 3 | 2018 | 17 | |
| 4 | 2022 | 5 | |
| 5 | 2020 | 5 | |
| 6 | 2018 | 4 | |
| 7 | 2023 | 2 | |
| 8 | Cross Domain Imitation Learning | 2019 | 1 |
| 9 | A Lagrangian Perspective on Latent Variable Generative Models | 2018 | 1 |
| 10 | 2022 | 1 | |
| 11 | 2024 | 1 | |
| 12 | 2024 | 1 | |
| 13 | 2024 | 0 | |
| 14 | 2024 | 0 | |
| 15 | 2022 | 0 |
About Jiaming Song
Jiaming Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Electrical and Electronic Engineering and Aerospace Engineering, having authored 15 papers that have together received 214 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Human Pose and Action Recognition (2 papers), Anomaly Detection Techniques and Applications (2 papers), Radar Systems and Signal Processing (2 papers), Smart Grid and Power Systems (2 papers), Time Series Analysis and Forecasting (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (95 citations), Artificial Intelligence (117 citations), Computational Mathematics (2 citations), Signal Processing (24 citations) and Computer Graphics and Computer-Aided Design (6 citations). Jiaming Song has collaborated with scholars based in China, United States and Italy. Frequent co-authors include Stefano Ermon, Shengjia Zhao, Russell J. Stewart, Volodymyr Kuleshov, Hongyu Ren, Stefano Ermon, Mykel J. Kochenderfer, Bahjat Kawar, Yuanqing Wang and Feiyu Chen. Their work appears in journals such as Tsinghua Science & Technology, AI Magazine, Signal Processing, IET conference proceedings. and Uncertainty in Artificial Intelligence.
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.