Jiaming Song
Impact in
-
- Generative Adversarial Networks and Image Synthesis
- Human Pose and Action Recognition
Papers in
-
- Generative Adversarial Networks and Image Synthesis 3
- Human Pose and Action Recognition 2
-
- Explainable Artificial Intelligence (XAI) 2
- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Stefano Ermon (7 shared papers)Shengjia Zhao (4 shared papers)Volodymyr Kuleshov (2 shared papers)Hongyu Ren (2 shared papers)Russell J. Stewart (2 shared papers)Mykel J. Kochenderfer (1 shared paper)Kun Ho Kim (1 shared paper)Jochen Schröder (1 shared paper)
- Journals
- AI Magazine (1 paper)Signal Processing (1 paper)Tsinghua Science & Technology (1 paper)Uncertainty in Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- ChinaUnited StatesSweden
In The Last Decade
Jiaming Song
9 papers receiving 177 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 81
- Computational Mathematics 2
- Artificial Intelligence 104
- Signal Processing 22
- Automotive Engineering 18
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 23 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 | 118 | |
| 2 | Learning Hierarchical Features from Deep Generative Models | 2017 | 37 |
| 3 | 2018 | 16 | |
| 4 | 2020 | 5 | |
| 5 | 2018 | 3 | |
| 6 | Cross Domain Imitation Learning | 2019 | 1 |
| 7 | A Lagrangian Perspective on Latent Variable Generative Models | 2018 | 1 |
| 8 | 2024 | 1 | |
| 9 | 2023 | 1 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 0 | |
| 12 | 2022 | 0 | |
| 13 | 2024 | 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 13 papers that have together received 184 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Radar Systems and Signal Processing (2 papers), Smart Grid and Power Systems (2 papers), Time Series Analysis and Forecasting (2 papers), Human Pose and Action Recognition (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Anomaly Detection Techniques and Applications (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 (81 citations), Computational Mathematics (2 citations), Artificial Intelligence (104 citations), Signal Processing (22 citations) and Automotive Engineering (18 citations). Jiaming Song has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Stefano Ermon, Shengjia Zhao, Volodymyr Kuleshov, Hongyu Ren, Russell J. Stewart, Mykel J. Kochenderfer, Kun Ho Kim, Jochen Schröder, Qingsong Wang and Laurent Schmalen. Their work appears in journals such as AI Magazine, Signal Processing, Tsinghua Science & Technology, Uncertainty in Artificial Intelligence and arXiv (Cornell University).
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.