Hao Cheng
- Health Informatics top 0.5%
- Artificial Intelligence top 1%
- Topic Modeling 16
- Natural Language Processing Techniques 13
- Domain Adaptation and Few-Shot Learning 5
- Speech and dialogue systems 3
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- Advanced Image and Video Retrieval Techniques 9
- Image Retrieval and Classification Techniques 8
- Multimodal Machine Learning Applications 6
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- Data Management and Algorithms 6
- Co-authors
- Jianfeng GaoHoifung PoonTristan Naumann裕二 池谷Robert TinnNaoto UsuyamaXiaodong LiuMichael Lucas
- Journals
- Information Sciences (1 paper)Remote Sensing (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)
- Partner nations
- United StatesSingaporeChina
In The Last Decade
Hao Cheng
43 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Health Informatics 160
- Artificial Intelligence 1.3k
- Computer Vision and Pattern Recognition 362
- Health Information Management 45
- Computational Mathematics 5
Countries citing papers authored by Hao Cheng
This map shows the geographic impact of Hao 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 Hao Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hao Cheng more than expected).
Fields of papers citing papers by Hao Cheng
This network shows the impact of papers produced by Hao 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 Hao Cheng. The network helps show where Hao Cheng may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hao 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 | 2024 | 0 | |
| 2 | 2023 | 72 | |
| 3 | 2023 | 18 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 4 | |
| 7 | 2022 | 8 | |
| 8 | 2022 | 5 | |
| 9 | Mixture of Robust Experts (MoRE): A Flexible Defense Against Multiple Perturbations. | 2021 | 1 |
| 10 | 2021 | 21 | |
| 11 | 2021 | 61 | |
| 12 | 2021 | 14 | |
| 13 | 2019 | 9 | |
| 14 | 2016 | 7 | |
| 15 | Scalable and Sound Low-Rank Tensor Learning | 2016 | 6 |
| 16 | 2015 | 131 | |
| 17 | Characterizing the Representer Theorem | 2013 | 6 |
| 18 | 2011 | 7 | |
| 19 | 2009 | 6 | |
| 20 | 2006 | 15 |
About Hao Cheng
Hao Cheng is a scholar working on Acoustics and Ultrasonics, Computational Mathematics and Artificial Intelligence, having authored 44 papers that have together received 1.8k indexed citations. Recurring topics across this work include Topic Modeling (16 papers), Natural Language Processing Techniques (13 papers), Advanced Image and Video Retrieval Techniques (9 papers), Image Retrieval and Classification Techniques (8 papers), Multimodal Machine Learning Applications (6 papers), Data Management and Algorithms (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Speech and dialogue systems (3 papers). The work is most often cited by research in Health Informatics (160 citations), Artificial Intelligence (1.3k citations) and Computer Vision and Pattern Recognition (362 citations). Hao Cheng has collaborated with scholars based in United States, Singapore and China. Frequent co-authors include Jianfeng Gao, Hoifung Poon, Tristan Naumann, 裕二 池谷, Robert Tinn, Naoto Usuyama, Xiaodong Liu, Michael Lucas, Mari Ostendorf and Kien A. Hua. Their work appears in journals such as Information Sciences, Remote Sensing and IEEE Transactions on Neural Networks and Learning Systems.
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.