Kaidi Cao
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 10%
- Molecular Biology
- Biomedical Engineering
- Computational Mechanics
- Co-authors
- Juan Carlos NieblesJingwei JiChien-Yi ChangZhangjie CaoChen Change LoyJing LiaoLu YuanJure Leskovec
- Topics
- Multimodal Machine Learning Applications (4 papers)Machine Learning and Data Classification (3 papers)Advanced Graph Neural Networks (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignArtificial Intelligence
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Kaidi Cao
16 papers receiving 511 citations
Peers
Comparison fields: 5 of 80
- Computer Vision and Pattern Recognition 364
- Artificial Intelligence 184
- Molecular Biology 44
- Biomedical Engineering 43
- Computational Mechanics 42
Countries citing papers authored by Kaidi Cao
This map shows the geographic impact of Kaidi Cao'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 Kaidi Cao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaidi Cao more than expected).
Fields of papers citing papers by Kaidi Cao
This network shows the impact of papers produced by Kaidi Cao. 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 Kaidi Cao. The network helps show where Kaidi Cao may publish in the future.
Co-authorship network of co-authors of Kaidi Cao
This figure shows the co-authorship network connecting the top 25 collaborators of Kaidi Cao. A scholar is included among the top collaborators of Kaidi Cao 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 Kaidi Cao. Kaidi Cao 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 | 44 | |
| 4 | 7 | |
| 5 | 37 | |
| 6 | 11 | |
| 7 | 5 | |
| 8 | Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization | 2 |
| 9 | Coresets for Robust Training of Deep Neural Networks against Noisy Labels. | 25 |
| 10 | Interstellar | 28 |
| 11 | 4 | |
| 12 | 1 | |
| 13 | 153 | |
| 14 | 27 | |
| 15 | 40 | |
| 16 | 67 | |
| 17 | 60 | |
| 18 | 8 |
About Kaidi Cao
Kaidi Cao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics, having authored 18 papers that have together received 519 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Machine Learning and Data Classification (3 papers) and Advanced Graph Neural Networks (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (364 citations), Computer Graphics and Computer-Aided Design (25 citations) and Artificial Intelligence (184 citations). Kaidi Cao has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Juan Carlos Niebles, Jingwei Ji, Chien-Yi Chang, Zhangjie Cao, Chen Change Loy, Jing Liao, Lu Yuan, Jure Leskovec, Wayne Wu and Chen Qian. Their work appears in journals such as Nature Methods, The British Journal of Psychiatry and ACM Transactions on Graphics.
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.