Xiuyuan Cheng
- Materials Chemistry
- Molecular Biology
- Artificial Intelligence
- Statistics and Probability top 10%
- Computer Vision and Pattern Recognition
- Topics
- Topological and Geometric Data Analysis (6 papers)Model Reduction and Neural Networks (5 papers)Random Matrices and Applications (4 papers)
- Partner nations
- United StatesChinaFrance
In The Last Decade
Xiuyuan Cheng
38 papers receiving 399 citations
Peers
Comparison fields: 5 of 108
- Materials Chemistry 103
- Molecular Biology 75
- Artificial Intelligence 65
- Statistics and Probability 47
- Computer Vision and Pattern Recognition 43
Countries citing papers authored by Xiuyuan Cheng
This map shows the geographic impact of Xiuyuan Cheng'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 Xiuyuan Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiuyuan Cheng more than expected).
Fields of papers citing papers by Xiuyuan Cheng
This network shows the impact of papers produced by Xiuyuan Cheng. 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 Xiuyuan Cheng. The network helps show where Xiuyuan Cheng may publish in the future.
Co-authorship network of co-authors of Xiuyuan Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Xiuyuan Cheng. A scholar is included among the top collaborators of Xiuyuan Cheng 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 Xiuyuan Cheng. Xiuyuan Cheng 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 | 2 | |
| 3 | 0 | |
| 4 | 12 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 68 | |
| 9 | Graph Convolution with Low-rank Learnable Local Filters | 1 |
| 10 | Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks | 3 |
| 11 | 2 | |
| 12 | 6 | |
| 13 | DCFNet: Deep Neural Network with Decomposed Convolutional Filters | 4 |
| 14 | Exact Recovery of Number of Blocks in Blockmodels | 2 |
| 15 | 5 | |
| 16 | 8 | |
| 17 | 9 | |
| 18 | 3 | |
| 19 | Expected performance bounds for estimation on graphs from random relative measurements. | 1 |
| 20 | 107 |
About Xiuyuan Cheng
Xiuyuan Cheng is a scholar working on Statistics and Probability, Structural Biology and Statistical and Nonlinear Physics, having authored 42 papers that have together received 413 indexed citations. Recurring topics across this work include Topological and Geometric Data Analysis (6 papers), Model Reduction and Neural Networks (5 papers) and Random Matrices and Applications (4 papers). The work is most often cited by research in Statistics and Probability (47 citations), Structural Biology (8 citations) and Surfaces, Coatings and Films (23 citations). Xiuyuan Cheng has collaborated with scholars based in United States, China and France. Frequent co-authors include E Weinan, Pingwen Zhang, An‐Chang Shi, Ling Lin, Amit Singer, Ruaidhrí Jackson, Jun Zhao, Ariel Jaffe, Esen Sefik and Richard A. Flavell. Their work appears in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nature Biotechnology.
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.