Dingheng Wang

483 total citations
15 papers, 304 citations indexed

About

Dingheng Wang is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Dingheng Wang has authored 15 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computational Mathematics, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Artificial Intelligence. Recurrent topics in Dingheng Wang's work include Tensor decomposition and applications (10 papers), Advanced Neural Network Applications (5 papers) and Advanced Memory and Neural Computing (3 papers). Dingheng Wang is often cited by papers focused on Tensor decomposition and applications (10 papers), Advanced Neural Network Applications (5 papers) and Advanced Memory and Neural Computing (3 papers). Dingheng Wang collaborates with scholars based in China, United States and Japan. Dingheng Wang's co-authors include Guoqi Li, Guangshe Zhao, Lei Deng, Man Yao, Zhao-Xu Yang, Yihan Lin, Yang Wu, Tianyi Yan, Zhexian Liu and Carsten M. Nielsen and has published in prestigious journals such as Nature Communications, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.

In The Last Decade

Dingheng Wang

14 papers receiving 297 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dingheng Wang China 9 136 116 91 88 66 15 304
Maohua Zhu China 8 178 1.3× 120 1.0× 42 0.5× 94 1.1× 22 0.3× 17 362
Zhenzhi Wu China 7 275 2.0× 178 1.5× 111 1.2× 78 0.9× 7 0.1× 26 405
Man Yao China 7 274 2.0× 127 1.1× 175 1.9× 41 0.5× 12 0.2× 15 366
Urs Köster United States 7 35 0.3× 80 0.7× 73 0.8× 97 1.1× 7 0.1× 8 236
Zheng Qu United States 9 114 0.8× 137 1.2× 20 0.2× 120 1.4× 16 0.2× 17 294
Shengyuan Zhou China 8 213 1.6× 122 1.1× 38 0.4× 173 2.0× 12 0.2× 14 378
Matthieu Courbariaux Canada 3 167 1.2× 219 1.9× 14 0.2× 311 3.5× 5 0.1× 4 436
Youngeun Kim United States 8 169 1.2× 211 1.8× 94 1.0× 119 1.4× 3 0.0× 17 360
Yoshikazu Washizawa Japan 9 34 0.3× 49 0.4× 151 1.7× 65 0.7× 21 0.3× 38 302
Kailash Gopalakrishnan India 6 170 1.3× 128 1.1× 5 0.1× 148 1.7× 10 0.2× 7 300

Countries citing papers authored by Dingheng Wang

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Dingheng Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Dingheng Wang. A scholar is included among the top collaborators of Dingheng Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dingheng Wang. Dingheng Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
2.
Zong, Changfu, et al.. (2024). Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis. World Electric Vehicle Journal. 15(5). 198–198. 2 indexed citations
3.
Yao, Man, Guangshe Zhao, Ning Qiao, et al.. (2024). Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nature Communications. 15(1). 4464–4464. 45 indexed citations
4.
Yao, Man, Guangshe Zhao, Xiyu Zhang, et al.. (2023). Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition. Neural Networks. 166. 410–423. 16 indexed citations
5.
Lv, Rui, Dingheng Wang, Jiangbin Zheng, & Zhao-Xu Yang. (2023). 3D-KCPNet: Efficient 3DCNNs based on tensor mapping theory. Neurocomputing. 565. 126985–126985. 1 indexed citations
6.
Wu, Yang, Dingheng Wang, Xiaotong Lu, et al.. (2022). Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies. arXiv (Cornell University). 19(5). 366–411. 12 indexed citations
7.
Wang, Dingheng, et al.. (2022). Realistic acceleration of neural networks with fine-grained tensor decomposition. Neurocomputing. 512. 52–68. 5 indexed citations
8.
Wang, Dingheng, et al.. (2021). QTTNet: Quantized tensor train neural networks for 3D object and video recognition. Neural Networks. 141. 420–432. 21 indexed citations
9.
Wang, Dingheng, et al.. (2021). Nonlinear tensor train format for deep neural network compression. Neural Networks. 144. 320–333. 23 indexed citations
10.
Wang, Dingheng, Guangshe Zhao, Man Yao, et al.. (2021). Kronecker CP Decomposition With Fast Multiplication for Compressing RNNs. IEEE Transactions on Neural Networks and Learning Systems. 34(5). 2205–2219. 16 indexed citations
11.
Yao, Man, Guangshe Zhao, Dingheng Wang, et al.. (2021). Temporal-wise Attention Spiking Neural Networks for Event Streams Classification. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10201–10210. 98 indexed citations
12.
Wang, Dingheng, et al.. (2020). Hybrid tensor decomposition in neural network compression. Neural Networks. 132. 309–320. 35 indexed citations
13.
Wang, Dingheng, Guangshe Zhao, Guoqi Li, Lei Deng, & Yang Wu. (2020). Compressing 3DCNNs based on tensor train decomposition. Neural Networks. 131. 215–230. 24 indexed citations
14.
Wang, Dingheng, et al.. (2019). Lossless Compression for 3DCNNs Based on Tensor Train Decomposition.. arXiv (Cornell University). 1 indexed citations
15.
Wang, Junli, Guangshe Zhao, Dingheng Wang, & Guoqi Li. (2019). Tensor Completion using Low-Rank Tensor Train Decomposition by Riemannian optimization. 3380–3384. 5 indexed citations

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.

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