Ke Bai
- Mechanical Engineering
- Biomedical Engineering
- Statistics, Probability and Uncertainty top 10%
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Co-authors
- Simeng ChenYihui ZhangYang LiYonggang HuangHaoran FuHao WangXu ChengJohn A. Rogers
- Topics
- Topic Modeling (3 papers)Multimodal Machine Learning Applications (3 papers)Natural Language Processing Techniques (3 papers)
- Cited by
- Statistics, Probability and UncertaintyRadiological and Ultrasound TechnologyMedical Laboratory Technology
- Journals
- Renewable EnergyJournal of the Mechanics and Physics of SolidsProcess Safety and Environmental Protection
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Ke Bai
15 papers receiving 94 citations
Peers
Comparison fields: 5 of 45
- Mechanical Engineering 43
- Biomedical Engineering 31
- Statistics, Probability and Uncertainty 17
- Artificial Intelligence 11
- Computer Vision and Pattern Recognition 9
Countries citing papers authored by Ke Bai
This map shows the geographic impact of Ke Bai'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 Ke Bai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ke Bai more than expected).
Fields of papers citing papers by Ke Bai
This network shows the impact of papers produced by Ke Bai. 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 Ke Bai. The network helps show where Ke Bai may publish in the future.
Co-authorship network of co-authors of Ke Bai
This figure shows the co-authorship network connecting the top 25 collaborators of Ke Bai. A scholar is included among the top collaborators of Ke Bai 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 Ke Bai. Ke Bai 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 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 5 | |
| 9 | 3 | |
| 10 | 11 | |
| 11 | 7 | |
| 12 | 3 | |
| 13 | 25 | |
| 14 | Variational annealing of GANs: A Langevin perspective | 1 |
| 15 | On Fenchel Mini-Max Learning | 1 |
| 16 | 32 | |
| 17 | 2 | |
| 18 | 1 |
About Ke Bai
Ke Bai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiological and Ultrasound Technology, having authored 18 papers that have together received 96 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Multimodal Machine Learning Applications (3 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (17 citations), Radiological and Ultrasound Technology (8 citations) and Medical Laboratory Technology (2 citations). Ke Bai has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Simeng Chen, Yihui Zhang, Yang Li, Yonggang Huang, Haoran Fu, Hao Wang, Xu Cheng, John A. Rogers, Xiaodong He and Liping Shi. Their work appears in journals such as Renewable Energy, Journal of the Mechanics and Physics of Solids and Process Safety and Environmental Protection.
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.