Jiaming Song
- Structural Biology top 10%
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- Generative Adversarial Networks and Image Synthesis 8
- Control and Systems Engineering top 10%
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications 6
- Adversarial Robustness in Machine Learning 5
- Domain Adaptation and Few-Shot Learning 5
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- Advanced Photocatalysis Techniques 8
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- 2D Materials and Applications 7
- ZnO doping and properties 7
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- Perovskite Materials and Applications 5
- Journals
- Journal of Alloys and Compounds (4 papers)The Journal of Physical Chemistry C (2 papers)Process Safety and Environmental Protection (2 papers)
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Jiaming Song
72 papers receiving 703 citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Structural Biology 27
- Computer Vision and Pattern Recognition 215
- Control and Systems Engineering 121
- Artificial Intelligence 164
- Renewable Energy, Sustainability and the Environment 75
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-authorship network
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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 6 | |
| 7 | 2024 | 0 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 7 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 10 | |
| 12 | 2022 | 0 | |
| 13 | 2022 | 2 | |
| 14 | 2021 | 11 | |
| 15 | D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation | 2021 | 32 |
| 16 | Imitation with Neural Density Models | 2021 | 3 |
| 17 | Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting | 2019 | 6 |
| 18 | InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations | 2017 | 47 |
| 19 | Generative Adversarial Learning of Markov Chains | 2017 | 0 |
| 20 | A-NICE-MC: Adversarial Training for MCMC | 2017 | 16 |
About Jiaming Song
Jiaming Song is a scholar working on Structural Biology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 82 papers that have together received 729 indexed citations. Recurring topics across this work include Advanced Photocatalysis Techniques (8 papers), Generative Adversarial Networks and Image Synthesis (8 papers), 2D Materials and Applications (7 papers), ZnO doping and properties (7 papers), Anomaly Detection Techniques and Applications (6 papers), Adversarial Robustness in Machine Learning (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Perovskite Materials and Applications (5 papers). The work is most often cited by research in Structural Biology (27 citations), Computer Vision and Pattern Recognition (215 citations) and Control and Systems Engineering (121 citations). Jiaming Song has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Stefano Ermon, Feng Teng, Ye Yuan, Jan Kautz, Umar Iqbal, Arash Vahdat, Haibo Fan, Yunzhu Li, Peng Hu and Shengjia Zhao. Their work appears in journals such as Journal of Alloys and Compounds, The Journal of Physical Chemistry C, Process Safety and Environmental Protection, Materials Science in Semiconductor Processing and Journal of Materials Chemistry C.
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.