Stephan Zheng
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Electrical and Electronic Engineering
- Management Science and Operations Research
- Co-authors
- Yang SongThomas LeungIan GoodfellowYisong YueAnima AnandkumarRichard SocherRose YuDavid C. Parkes
- Topics
- Reinforcement Learning in Robotics (3 papers)Particle Detector Development and Performance (2 papers)Adversarial Robustness in Machine Learning (2 papers)
- Journals
- Nature CommunicationsSHILAP Revista de lepidopterologíaScience Advances
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Stephan Zheng
14 papers receiving 547 citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 300
- Computer Vision and Pattern Recognition 136
- Signal Processing 71
- Electrical and Electronic Engineering 47
- Management Science and Operations Research 35
Countries citing papers authored by Stephan Zheng
This map shows the geographic impact of Stephan Zheng'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 Stephan Zheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephan Zheng more than expected).
Fields of papers citing papers by Stephan Zheng
This network shows the impact of papers produced by Stephan Zheng. 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 Stephan Zheng. The network helps show where Stephan Zheng may publish in the future.
Co-authorship network of co-authors of Stephan Zheng
This figure shows the co-authorship network connecting the top 25 collaborators of Stephan Zheng. A scholar is included among the top collaborators of Stephan Zheng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Stephan Zheng. Stephan Zheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 51 | |
| 3 | 78 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 13 | |
| 7 | Generating Multi-Agent Trajectories using Programmatic Weak Supervision | 4 |
| 8 | 12 | |
| 9 | Structured Exploration via Hierarchical Variational Policy Networks | 1 |
| 10 | Long Range Sequence Generation via Multiresolution Adversarial Training | 1 |
| 11 | 22 | |
| 12 | Measuring the Robustness of Neural Networks via Minimal Adversarial Examples | 1 |
| 13 | Long-term Forecasting using Tensor-Train RNNs | 55 |
| 14 | Improving the Robustness of Deep Neural Networks via Stability Trainingbreakdown → | 312 |
About Stephan Zheng
Stephan Zheng is a scholar working on Computational Mathematics, Health Informatics and Artificial Intelligence, having authored 14 papers that have together received 560 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Particle Detector Development and Performance (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Computational Mathematics (18 citations), Health Informatics (14 citations) and Artificial Intelligence (300 citations). Stephan Zheng has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Yang Song, Thomas Leung, Ian Goodfellow, Yisong Yue, Anima Anandkumar, Richard Socher, Rose Yu, David C. Parkes, Sunil Srinivasa and Adam Gibson. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Science Advances.
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.