Yanghan Wang
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
Papers in
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- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 1
- Advanced Vision and Imaging 1
- Advanced Image Processing Techniques 1
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- Domain Adaptation and Few-Shot Learning 1
- Machine Learning and ELM 1
- Co-authors
- Bichen Wu (2 shared papers)Yiming Wu (2 shared papers)Xiaoliang Dai (2 shared papers)Péter Vajda (2 shared papers)Yangqing Jia (2 shared papers)Peizhao Zhang (2 shared papers)Fei Sun (2 shared papers)Yuandong Tian (1 shared paper)
- Partner nations
- United StatesIsraelFinland
In The Last Decade
Yanghan Wang
3 papers receiving 877 citations
Yanghan Wang's Hit Papers
Peers
Comparison fields: 5 of 70
- Computer Vision and Pattern Recognition 707
- Artificial Intelligence 541
- Hardware and Architecture 38
- Neurology 26
- Computational Mathematics 2
Countries citing papers authored by Yanghan Wang
This map shows the geographic impact of Yanghan Wang'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 Yanghan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yanghan Wang more than expected).
Fields of papers citing papers by Yanghan Wang
This network shows the impact of papers produced by Yanghan Wang. 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 Yanghan Wang. The network helps show where Yanghan Wang may publish in the future.
Co-authors
The 23 scholars most cited alongside Yanghan Wang, 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 | FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search Hit paper breakdown → | 2019 | 740 |
| 2 | 2019 | 160 | |
| 3 | 2023 | 4 |
About Yanghan Wang
Yanghan Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Electrical and Electronic Engineering and Infectious Diseases, having authored 3 papers that have together received 904 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Advanced Vision and Imaging (1 paper), Image Processing Techniques and Applications (1 paper), Advanced Image Processing Techniques (1 paper), Machine Learning and ELM (1 paper) and Advanced Memory and Neural Computing (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (707 citations), Artificial Intelligence (541 citations), Hardware and Architecture (38 citations), Neurology (26 citations) and Computational Mathematics (2 citations). Yanghan Wang has collaborated with scholars based in United States, Israel and Finland. Frequent co-authors include Bichen Wu, Yiming Wu, Xiaoliang Dai, Péter Vajda, Yangqing Jia, Peizhao Zhang, Fei Sun, Yuandong Tian, Kurt Keutzer and Matt Uyttendaele.
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.