Danlu Chen
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Video Surveillance and Tracking Methods
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
Papers in
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- Image Processing and 3D Reconstruction 2
- Advanced Neural Network Applications 2
- Handwritten Text Recognition Techniques 1
- Digital Imaging for Blood Diseases 1
- Currency Recognition and Detection 1
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- Domain Adaptation and Few-Shot Learning 2
- Natural Language Processing Techniques 1
- Co-authors
- Laurens van der Maaten (2 shared papers)Tianhong Li (2 shared papers)Gao Huang (2 shared papers)Kilian Q. Weinberger (2 shared papers)Felix Wu (2 shared papers)Xiang Zhang (1 shared paper)Taylor Berg-Kirkpatrick (2 shared papers)David A. Smith (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)arXiv (Cornell University) (2 papers)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United StatesNetherlands
In The Last Decade
Danlu Chen
3 papers receiving 198 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 151
- Artificial Intelligence 111
- Media Technology 17
- Neurology 9
- Space and Planetary Science 1
Countries citing papers authored by Danlu Chen
This map shows the geographic impact of Danlu Chen'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 Danlu Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danlu Chen more than expected).
Fields of papers citing papers by Danlu Chen
This network shows the impact of papers produced by Danlu Chen. 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 Danlu Chen. The network helps show where Danlu Chen may publish in the future.
Co-authors
The 8 scholars most cited alongside Danlu Chen, 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 | 2017 | 131 | |
| 2 | Multi-Scale Dense Convolutional Networks for Efficient Prediction. | 2017 | 73 |
| 3 | 2024 | 1 | |
| 4 | 2023 | 0 |
About Danlu Chen
Danlu Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Infectious Diseases and Organic Chemistry, having authored 4 papers that have together received 205 indexed citations. Recurring topics across this work include Image Processing and 3D Reconstruction (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Handwritten Text Recognition Techniques (1 paper), Natural Language Processing Techniques (1 paper), Digital Imaging for Blood Diseases (1 paper), Time Series Analysis and Forecasting (1 paper) and Currency Recognition and Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (151 citations), Artificial Intelligence (111 citations), Media Technology (17 citations), Neurology (9 citations) and Space and Planetary Science (1 citation). Danlu Chen has collaborated with scholars based in United States and Netherlands. Frequent co-authors include Laurens van der Maaten, Tianhong Li, Gao Huang, Kilian Q. Weinberger, Felix Wu, Xiang Zhang, Taylor Berg-Kirkpatrick and David A. Smith. Their work appears in journals such as Lecture notes in computer science, arXiv (Cornell University) and DOAJ (DOAJ: Directory of Open Access Journals).
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.