Kai Yu
Impact in
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- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning
- Text and Document Classification Technologies
Papers in
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- Bayesian Methods and Mixture Models 8
- Gaussian Processes and Bayesian Inference 7
- Machine Learning and Algorithms 6
- Topic Modeling 6
- Domain Adaptation and Few-Shot Learning 5
- Machine Learning and Data Classification 5
- Neural Networks and Applications 5
- Software 4
- Co-authors
- Volker TrespYihong GongTong ZhangShipeng YuAnton SchwaighoferShenghuo ZhuHans‐Peter KriegelJinbo Bi
- Journals
- Pharmacogenomics (2 papers)Journal of Biomedical Informatics (1 paper)IEEE Access (1 paper)Knowledge and Information Systems (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- GermanyUnited StatesChina
In The Last Decade
Kai Yu
46 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 131
- Computer Vision and Pattern Recognition 1.1k
- Artificial Intelligence 1.7k
- Computational Mathematics 20
- Information Systems 533
- Statistical and Nonlinear Physics 260
Countries citing papers authored by Kai Yu
This map shows the geographic impact of Kai Yu'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 Kai Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Yu more than expected).
Fields of papers citing papers by Kai Yu
This network shows the impact of papers produced by Kai Yu. 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 Kai Yu. The network helps show where Kai Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kai Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2023 | 5 | |
| 3 | 2020 | 5 | |
| 4 | 2018 | 0 | |
| 5 | Communication Efficient Distributed Machine Learning with the Parameter Server | 2014 | 242 |
| 6 | 2012 | 6 | |
| 7 | 2012 | 1 | |
| 8 | Nonlinear Learning using Local Coordinate Coding Hit paper breakdown → | 2009 | 520 |
| 9 | Stochastic Relational Models for Large-scale Dyadic Data using MCMC | 2008 | 15 |
| 10 | Deep Learning with Kernel Regularization for Visual Recognition | 2008 | 54 |
| 11 | 2008 | 98 | |
| 12 | Gaussian Process Models for Link Analysis and Transfer Learning | 2007 | 33 |
| 13 | Fast Inference in Infinite Hidden Relational Models. | 2007 | 10 |
| 14 | 2007 | 175 | |
| 15 | Infinite hidden relational models | 2006 | 80 |
| 16 | Soft Clustering on Graphs | 2005 | 68 |
| 17 | 2005 | 1 | |
| 18 | Learning Gaussian Process Kernels via Hierarchical Bayes | 2004 | 112 |
| 19 | Dirichlet Enhanced Latent Semantic Analysis. | 2004 | 1 |
| 20 | 2002 | 2 |
About Kai Yu
Kai Yu is a scholar working on Artificial Intelligence, Software, Health Informatics, Information Systems and Signal Processing, having authored 48 papers that have together received 2.8k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (9 papers), Bayesian Methods and Mixture Models (8 papers), Gaussian Processes and Bayesian Inference (7 papers), Machine Learning and Algorithms (6 papers), Topic Modeling (6 papers), Domain Adaptation and Few-Shot Learning (5 papers), Machine Learning and Data Classification (5 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Artificial Intelligence (1.7k citations), Computational Mathematics (20 citations), Information Systems (533 citations) and Statistical and Nonlinear Physics (260 citations). Kai Yu has collaborated with scholars based in Germany, United States and China. Frequent co-authors include Volker Tresp, Yihong Gong, Tong Zhang, Shipeng Yu, Anton Schwaighofer, Shenghuo Zhu, Hans‐Peter Kriegel, Jinbo Bi, Mu Li and David G. Andersen. Their work appears in journals such as Pharmacogenomics, Journal of Biomedical Informatics, IEEE Access, Knowledge and Information Systems and arXiv (Cornell University).
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.