Dingheng Wang
Impact in
- Computational Mathematics top 2%
- Tensor decomposition and applications
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- Neural dynamics and brain function
Papers in
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- Tensor decomposition and applications 10
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- Advanced Neural Network Applications 5
- Human Pose and Action Recognition 3
- Co-authors
- Guoqi Li (11 shared papers)Guangshe Zhao (10 shared papers)Lei Deng (6 shared papers)Man Yao (4 shared papers)Zhao-Xu Yang (3 shared papers)Yihan Lin (1 shared paper)Yang Wu (2 shared papers)Tianyi Yan (2 shared papers)
- Journals
- Neural Networks (5 papers)Neurocomputing (3 papers)Nature Communications (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)World Electric Vehicle Journal (1 paper)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Dingheng Wang
14 papers receiving 297 citations
Peers
Comparison fields: 5 of 51
- Computational Mathematics 66
- Cognitive Neuroscience 91
- Computer Vision and Pattern Recognition 88
- Artificial Intelligence 116
- Hardware and Architecture 22
Countries citing papers authored by Dingheng Wang
This map shows the geographic impact of Dingheng 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 Dingheng Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dingheng Wang more than expected).
Fields of papers citing papers by Dingheng Wang
This network shows the impact of papers produced by Dingheng 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 Dingheng Wang. The network helps show where Dingheng Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Dingheng 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 | 2021 | 98 | |
| 2 | 2024 | 45 | |
| 3 | 2020 | 35 | |
| 4 | 2020 | 24 | |
| 5 | 2021 | 23 | |
| 6 | 2021 | 21 | |
| 7 | 2023 | 16 | |
| 8 | 2021 | 16 | |
| 9 | 2022 | 12 | |
| 10 | 2019 | 5 | |
| 11 | 2022 | 5 | |
| 12 | 2024 | 2 | |
| 13 | Lossless Compression for 3DCNNs Based on Tensor Train Decomposition. | 2019 | 1 |
| 14 | 2023 | 1 | |
| 15 | 2025 | 0 |
About Dingheng Wang
Dingheng Wang is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Electrical and Electronic Engineering, having authored 15 papers that have together received 304 indexed citations. Recurring topics across this work include Tensor decomposition and applications (10 papers), Advanced Neural Network Applications (5 papers), Advanced Neuroimaging Techniques and Applications (3 papers), Human Pose and Action Recognition (3 papers), Advanced Memory and Neural Computing (3 papers), Ferroelectric and Negative Capacitance Devices (2 papers), Neural Networks and Reservoir Computing (2 papers) and Parallel Computing and Optimization Techniques (2 papers). The work is most often cited by research in Computational Mathematics (66 citations), Cognitive Neuroscience (91 citations), Computer Vision and Pattern Recognition (88 citations), Artificial Intelligence (116 citations) and Hardware and Architecture (22 citations). Dingheng Wang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Guoqi Li, Guangshe Zhao, Lei Deng, Man Yao, Zhao-Xu Yang, Yihan Lin, Yang Wu, Tianyi Yan, Zhexian Liu and Sadique Sheik. Their work appears in journals such as Neural Networks, Neurocomputing, Nature Communications, IEEE Transactions on Neural Networks and Learning Systems and World Electric Vehicle Journal.
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.