Yinpeng Dong
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 1%
- Signal Processing top 2%
- Molecular Biology
- Hardware and Architecture top 5%
- Topics
- Adversarial Robustness in Machine Learning (27 papers)Anomaly Detection Techniques and Applications (11 papers)Domain Adaptation and Few-Shot Learning (10 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceInformation Sciences
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Yinpeng Dong
35 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 1.2k
- Computer Vision and Pattern Recognition 688
- Signal Processing 292
- Molecular Biology 133
- Hardware and Architecture 97
Countries citing papers authored by Yinpeng Dong
This map shows the geographic impact of Yinpeng Dong'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 Yinpeng Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yinpeng Dong more than expected).
Fields of papers citing papers by Yinpeng Dong
This network shows the impact of papers produced by Yinpeng Dong. 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 Yinpeng Dong. The network helps show where Yinpeng Dong may publish in the future.
Co-authorship network of co-authors of Yinpeng Dong
This figure shows the co-authorship network connecting the top 25 collaborators of Yinpeng Dong. A scholar is included among the top collaborators of Yinpeng Dong 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 Yinpeng Dong. Yinpeng Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 14 | |
| 7 | 5 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 1 | |
| 11 | 7 | |
| 12 | 17 | |
| 13 | 18 | |
| 14 | 66 | |
| 15 | Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness | 5 |
| 16 | Boosting Adversarial Training with Hypersphere Embedding | 12 |
| 17 | 133 | |
| 18 | Improving Black-box Adversarial Attacks with a Transfer-based Prior | 24 |
| 19 | Discovering Adversarial Examples with Momentum | 30 |
| 20 | Crowd scene understanding with coherent recurrent neural networks | 28 |
About Yinpeng Dong
Yinpeng Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 40 papers that have together received 1.5k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (27 papers), Anomaly Detection Techniques and Applications (11 papers) and Domain Adaptation and Few-Shot Learning (10 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computer Vision and Pattern Recognition (688 citations) and Signal Processing (292 citations). Yinpeng Dong has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Jun Zhu, Hang Su, Tianyu Pang, Zhifeng Li, Wei Liu, Baoyuan Wu, Tong Zhang, Hang Su, Zihao Xiao and Xiao Yang. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Information Sciences.
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.