Xiaohu Cheng
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
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- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
Papers in
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- Sentiment Analysis and Opinion Mining 2
- Topic Modeling 2
- Domain Adaptation and Few-Shot Learning 2
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- Multimodal Machine Learning Applications 2
- Co-authors
- Fuzhen Zhuang (4 shared papers)Qing He (3 shared papers)Ping Luo (2 shared papers)Sinno Jialin Pan (2 shared papers)Zhongzhi Shi (2 shared papers)Qing He (2 shared papers)Fuzhen Zhuang (1 shared paper)Kun Ma (2 shared papers)
- Journals
- ACM Transactions on Intelligent Systems and Technology (1 paper)Sensors and Actuators B Chemical (1 paper)Colloids and Surfaces B Biointerfaces (1 paper)Transducer and Microsystem Technologies (1 paper)Angewandte Chemie (1 paper)
In The Last Decade
Xiaohu Cheng
8 papers receiving 268 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 187
- Computer Vision and Pattern Recognition 104
- Signal Processing 16
- Cancer Research 14
- Information Systems 23
Countries citing papers authored by Xiaohu Cheng
This map shows the geographic impact of Xiaohu 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 Xiaohu Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaohu Cheng more than expected).
Fields of papers citing papers by Xiaohu Cheng
This network shows the impact of papers produced by Xiaohu 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 Xiaohu Cheng. The network helps show where Xiaohu Cheng may publish in the future.
Co-authors
The 25 scholars most cited alongside Xiaohu Cheng, 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 | Supervised representation learning: transfer learning with deep autoencoders | 2015 | 191 |
| 2 | 2017 | 40 | |
| 3 | 2023 | 16 | |
| 4 | 2020 | 13 | |
| 5 | 2014 | 10 | |
| 6 | 2014 | 6 | |
| 7 | Method for weight decision of WSNs performance index based on entropy weight method | 2013 | 1 |
| 8 | 2014 | 1 | |
| 9 | 2025 | 1 | |
| 10 | 2025 | 0 |
About Xiaohu Cheng
Xiaohu Cheng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Biomedical Engineering, Materials Chemistry and Control and Systems Engineering, having authored 10 papers that have together received 279 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Topic Modeling (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Gold and Silver Nanoparticles Synthesis and Applications (1 paper), Laser-Ablation Synthesis of Nanoparticles (1 paper), Advanced Nanomaterials in Catalysis (1 paper) and Graphene and Nanomaterials Applications (1 paper). The work is most often cited by research in Artificial Intelligence (187 citations), Computer Vision and Pattern Recognition (104 citations), Signal Processing (16 citations), Cancer Research (14 citations) and Information Systems (23 citations). Xiaohu Cheng has collaborated with scholars based in China, Singapore and Italy. Frequent co-authors include Fuzhen Zhuang, Qing He, Ping Luo, Sinno Jialin Pan, Zhongzhi Shi, Qing He, Fuzhen Zhuang, Kun Ma, Dongbo Xi and Fen Lin. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, Sensors and Actuators B Chemical, Colloids and Surfaces B Biointerfaces, Transducer and Microsystem Technologies and Angewandte Chemie.
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.