Dong Yin
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Computational Mechanics
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Kannan RamchandranPeter L. BartlettYudong ChenXin ZhanRong ZhangYibing ZhanRamtin PedarsaniChristopher J. Rozell
- Topics
- Sparse and Compressive Sensing Techniques (6 papers)Advanced Image Fusion Techniques (2 papers)Medical Image Segmentation Techniques (2 papers)
- Partner nations
- ChinaUnited StatesNew Zealand
In The Last Decade
Dong Yin
27 papers receiving 214 citations
Peers
Comparison fields: 5 of 59
- Artificial Intelligence 82
- Computer Vision and Pattern Recognition 78
- Computational Mechanics 43
- Computer Networks and Communications 35
- Signal Processing 29
Countries citing papers authored by Dong Yin
This map shows the geographic impact of Dong Yin'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 Dong Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong Yin more than expected).
Fields of papers citing papers by Dong Yin
This network shows the impact of papers produced by Dong Yin. 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 Dong Yin. The network helps show where Dong Yin may publish in the future.
Co-authorship network of co-authors of Dong Yin
This figure shows the co-authorship network connecting the top 25 collaborators of Dong Yin. A scholar is included among the top collaborators of Dong Yin 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 Dong Yin. Dong Yin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 10 | |
| 3 | 4 | |
| 4 | 1 | |
| 5 | 6 | |
| 6 | 12 | |
| 7 | 1 | |
| 8 | Rademacher Complexity for Adversarially Robust Generalization | 14 |
| 9 | Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates | 38 |
| 10 | 1 | |
| 11 | 4 | |
| 12 | 7 | |
| 13 | The Design and Optimize of Equalization Schemes for Underwater Power LiFePO4 Battery Stack | 4 |
| 14 | 4 | |
| 15 | 7 | |
| 16 | 41 | |
| 17 | 0 | |
| 18 | 4 | |
| 19 | 2 | |
| 20 | 7 |
About Dong Yin
Dong Yin is a scholar working on Structural Biology, Computer Vision and Pattern Recognition and Media Technology, having authored 29 papers that have together received 224 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (6 papers), Advanced Image Fusion Techniques (2 papers) and Medical Image Segmentation Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (78 citations), Media Technology (25 citations) and Signal Processing (29 citations). Dong Yin has collaborated with scholars based in China, United States and New Zealand. Frequent co-authors include Kannan Ramchandran, Peter L. Bartlett, Yudong Chen, Xin Zhan, Rong Zhang, Rong Zhang, Yibing Zhan, Ramtin Pedarsani, Christopher J. Rozell and Xiaoning Peng. Their work appears in journals such as IEEE Transactions on Information Theory, Sustainability and Machine Learning.
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.