Mingxing Tan
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- Advanced Neural Network Applications 16
- Human Pose and Action Recognition 4
- Media Technology top 0.5%
- Aerospace Engineering top 1%
- Artificial Intelligence top 1%
- Domain Adaptation and Few-Shot Learning 7
- Adversarial Robustness in Machine Learning 5
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- Parallel Computing and Optimization Techniques 12
- Embedded Systems Design Techniques 10
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- Interconnection Networks and Systems 9
- Advanced Data Storage Technologies 5
Mingxing Tan
41 papers receiving 6.4k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Computer Vision and Pattern Recognition 4.2k
- Industrial and Manufacturing Engineering 839
- Media Technology 735
- Aerospace Engineering 998
- Artificial Intelligence 1.2k
Countries citing papers authored by Mingxing Tan
This map shows the geographic impact of Mingxing Tan'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 Mingxing Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingxing Tan more than expected).
Fields of papers citing papers by Mingxing Tan
This network shows the impact of papers produced by Mingxing Tan. 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 Mingxing Tan. The network helps show where Mingxing Tan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mingxing Tan, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 10 | |
| 4 | 2024 | 3 | |
| 5 | 2023 | 2 | |
| 6 | 2023 | 3 | |
| 7 | EfficientNetV2: Smaller Models and Faster Training | 2021 | 1 |
| 8 | CoAtNet: Marrying Convolution and Attention for All Data Sizes | 2021 | 1 |
| 9 | 2021 | 97 | |
| 10 | 2021 | 146 | |
| 11 | EfficientDet: Scalable and Efficient Object Detectionbreakdown → | 2020 | 5281 |
| 12 | AutoHAS: Differentiable Hyper-parameter and Architecture Search. | 2020 | 10 |
| 13 | Adversarial Examples Improve Image Recognitionbreakdown → | 2020 | 247 |
| 14 | MixConv: Mixed Depthwise Convolutional Kernels | 2019 | 21 |
| 15 | 2017 | 11 | |
| 16 | 2015 | 3 | |
| 17 | 2015 | 5 | |
| 18 | 2014 | 14 | |
| 19 | 2014 | 17 | |
| 20 | 2012 | 3 |
About Mingxing Tan
Mingxing Tan is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 43 papers that have together received 6.6k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (16 papers), Parallel Computing and Optimization Techniques (12 papers), Embedded Systems Design Techniques (10 papers), Interconnection Networks and Systems (9 papers), Domain Adaptation and Few-Shot Learning (7 papers), Adversarial Robustness in Machine Learning (5 papers), Advanced Data Storage Technologies (5 papers) and Human Pose and Action Recognition (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.2k citations), Industrial and Manufacturing Engineering (839 citations) and Media Technology (735 citations). Mingxing Tan has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Quoc V. Le, Ruoming Pang, Boqing Gong, Alan Yuille, Cihang Xie, Jiang Wang, Zhiru Zhang, Steve Dai, Adams Wei Yu and Vikas Singh. Their work appears in journals such as Neurocomputing, Journal of Coastal Research, IEEE Robotics and Automation Letters, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems and International Journal of Electrochemical Science.
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.