Chuan-Sheng Foo
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 5%
- Signal Processing top 5%
- Molecular Biology
- Co-authors
- Vijay ChandrasekharBruno LecouatHoussam ZenatiXiaoli LiWenyu ZhangSavitha RamasamyJules SamaranMin Wu
- Topics
- Domain Adaptation and Few-Shot Learning (16 papers)Multimodal Machine Learning Applications (9 papers)Advanced Neural Network Applications (8 papers)
- Partner nations
- SingaporeUnited StatesChina
In The Last Decade
Chuan-Sheng Foo
65 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 121
- Artificial Intelligence 656
- Computer Vision and Pattern Recognition 251
- Computer Networks and Communications 224
- Signal Processing 192
- Molecular Biology 188
Countries citing papers authored by Chuan-Sheng Foo
This map shows the geographic impact of Chuan-Sheng Foo'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 Chuan-Sheng Foo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chuan-Sheng Foo more than expected).
Fields of papers citing papers by Chuan-Sheng Foo
This network shows the impact of papers produced by Chuan-Sheng Foo. 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 Chuan-Sheng Foo. The network helps show where Chuan-Sheng Foo may publish in the future.
Co-authorship network of co-authors of Chuan-Sheng Foo
This figure shows the co-authorship network connecting the top 25 collaborators of Chuan-Sheng Foo. A scholar is included among the top collaborators of Chuan-Sheng Foo 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 Chuan-Sheng Foo. Chuan-Sheng Foo 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 | 1 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 11 | |
| 8 | 3 | |
| 9 | 0 | |
| 10 | 5 | |
| 11 | 11 | |
| 12 | 1 | |
| 13 | Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | 20 |
| 14 | Validation Free and Replication Robust Volume-based Data Valuation | 15 |
| 15 | 8 | |
| 16 | 17 | |
| 17 | Mirror descent in saddle-point problems: Going the extra (gradient) mile. | 9 |
| 18 | The Unusual Effectiveness of Averaging in GAN Training | 10 |
| 19 | 20 | |
| 20 | Efficient multiple hyperparameter learning for log-linear models | 46 |
About Chuan-Sheng Foo
Chuan-Sheng Foo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Geology, having authored 69 papers that have together received 1.2k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (16 papers), Multimodal Machine Learning Applications (9 papers) and Advanced Neural Network Applications (8 papers). The work is most often cited by research in Artificial Intelligence (656 citations), Signal Processing (192 citations) and Computer Vision and Pattern Recognition (251 citations). Chuan-Sheng Foo has collaborated with scholars based in Singapore, United States and China. Frequent co-authors include Vijay Chandrasekhar, Bruno Lecouat, Houssam Zenati, Xiaoli Li, Wenyu Zhang, Savitha Ramasamy, Jules Samaran, Min Wu, Zhenghua Chen and Mohamed Ragab. Their work appears in journals such as Nature Communications, Genes & Development and Bioinformatics.
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.