Ruichi Yu
Impact in
-
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
Papers in
-
- Video Surveillance and Tracking Methods 4
- Advanced Neural Network Applications 4
- Advanced Image and Video Retrieval Techniques 2
- Visual Attention and Saliency Detection 1
-
- Domain Adaptation and Few-Shot Learning 1
- Sentiment Analysis and Opinion Mining 1
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Larry S. Davis (6 shared papers)Vlad I. Morariu (4 shared papers)Mingfei Gao (2 shared papers)Jui-Hsin Lai (2 shared papers)Ching‐Yung Lin (3 shared papers)Chun-Fu Chen (1 shared paper)Xintong Han (1 shared paper)Ang Li (1 shared paper)
- Journals
- 2022 International Conference on Robotics and Automation (ICRA) (1 paper)RePEc: Research Papers in Economics (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Ruichi Yu
9 papers receiving 731 citations
Ruichi Yu's Hit Papers
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 590
- Computational Mathematics 8
- Geology 63
- Artificial Intelligence 359
- Computer Graphics and Computer-Aided Design 26
Countries citing papers authored by Ruichi Yu
This map shows the geographic impact of Ruichi 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 Ruichi Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruichi Yu more than expected).
Fields of papers citing papers by Ruichi Yu
This network shows the impact of papers produced by Ruichi 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 Ruichi Yu. The network helps show where Ruichi Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Ruichi 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 | NISP: Pruning Networks Using Neuron Importance Score Propagation Hit paper breakdown → | 2018 | 479 |
| 2 | 2019 | 108 | |
| 3 | 2018 | 91 | |
| 4 | 2016 | 23 | |
| 5 | 2018 | 20 | |
| 6 | 2017 | 15 | |
| 7 | 2022 | 11 | |
| 8 | 2017 | 2 | |
| 9 | 2014 | 1 |
About Ruichi Yu
Ruichi Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Statistical and Nonlinear Physics, Cognitive Neuroscience and Computational Mechanics, having authored 9 papers that have together received 750 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (4 papers), Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (2 papers), Visual Attention and Saliency Detection (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), 3D Surveying and Cultural Heritage (1 paper), Sentiment Analysis and Opinion Mining (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (590 citations), Computational Mathematics (8 citations), Geology (63 citations), Artificial Intelligence (359 citations) and Computer Graphics and Computer-Aided Design (26 citations). Ruichi Yu has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Larry S. Davis, Vlad I. Morariu, Mingfei Gao, Jui-Hsin Lai, Ching‐Yung Lin, Chun-Fu Chen, Xintong Han, Ang Li, Shiyi Lan and Gang Yu. Their work appears in journals such as 2022 International Conference on Robotics and Automation (ICRA) and RePEc: Research Papers in Economics.
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.