Shengyang Sun

848 total citations
23 papers, 343 citations indexed

About

Shengyang Sun is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Shengyang Sun has authored 23 papers receiving a total of 343 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Electrical and Electronic Engineering, 7 papers in Cognitive Neuroscience and 6 papers in Artificial Intelligence. Recurrent topics in Shengyang Sun's work include Advanced Memory and Neural Computing (11 papers), Neural dynamics and brain function (7 papers) and CCD and CMOS Imaging Sensors (7 papers). Shengyang Sun is often cited by papers focused on Advanced Memory and Neural Computing (11 papers), Neural dynamics and brain function (7 papers) and CCD and CMOS Imaging Sensors (7 papers). Shengyang Sun collaborates with scholars based in China, Canada and United States. Shengyang Sun's co-authors include Jiayi Zhang, Zhaocheng Wang, Linglong Dai, Qingjiang Li, Haijun Liu, Jiwei Li, Changyou Chen, Zhiwei Li, Lawrence Carin and Yue Zhou and has published in prestigious journals such as IEEE Access, Neurocomputing and IEEE Communications Letters.

In The Last Decade

Shengyang Sun

23 papers receiving 333 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shengyang Sun China 8 276 54 49 39 36 23 343
Junseok Kim South Korea 9 265 1.0× 107 2.0× 34 0.7× 45 1.2× 30 0.8× 24 331
Ashwin Sanjay Lele United States 9 117 0.4× 44 0.8× 49 1.0× 22 0.6× 14 0.4× 28 210
Chit-Kwan Lin United States 10 244 0.9× 112 2.1× 69 1.4× 37 0.9× 61 1.7× 15 316
James A. Hilder United Kingdom 9 74 0.3× 56 1.0× 61 1.2× 26 0.7× 20 0.6× 16 212
Muhammad Sohaib J. Solaija Türkiye 7 159 0.6× 52 1.0× 27 0.6× 9 0.2× 40 1.1× 14 261
Jong‐Hyeok Yoon South Korea 12 327 1.2× 16 0.3× 34 0.7× 51 1.3× 21 0.6× 39 365
Syed Kamran Haider China 9 104 0.4× 88 1.6× 80 1.6× 14 0.4× 50 1.4× 29 248
Dennis Walter Germany 11 255 0.9× 59 1.1× 25 0.5× 45 1.2× 12 0.3× 25 332
Haoyue Tang China 10 236 0.9× 241 4.5× 26 0.5× 33 0.8× 19 0.5× 27 400
Chia-Lin Cheng United States 11 294 1.1× 20 0.4× 29 0.6× 26 0.7× 42 1.2× 17 358

Countries citing papers authored by Shengyang Sun

Since Specialization
Citations

This map shows the geographic impact of Shengyang Sun'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 Shengyang Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shengyang Sun more than expected).

Fields of papers citing papers by Shengyang Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shengyang Sun. 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 Shengyang Sun. The network helps show where Shengyang Sun may publish in the future.

Co-authorship network of co-authors of Shengyang Sun

This figure shows the co-authorship network connecting the top 25 collaborators of Shengyang Sun. A scholar is included among the top collaborators of Shengyang Sun 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 Shengyang Sun. Shengyang Sun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sun, Shengyang & Xiaojin Gong. (2024). Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection. 1–6. 3 indexed citations
2.
Li, Jiwei, Hui Xu, Shengyang Sun, et al.. (2021). In Situ Learning in Hardware Compatible Multilayer Memristive Spiking Neural Network. IEEE Transactions on Cognitive and Developmental Systems. 14(2). 448–461. 11 indexed citations
3.
Liu, Haijun, et al.. (2020). Binary Memristive Synapse Based Vector Neural Network Architecture and Its Application. IEEE Transactions on Circuits & Systems II Express Briefs. 68(2). 772–776. 2 indexed citations
4.
Sun, Shengyang, et al.. (2019). Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. Neural Information Processing Systems. 32. 10802–10812. 2 indexed citations
5.
Sun, Shengyang, et al.. (2019). Quaternary synapses network for memristor-based spiking convolutional neural networks. IEICE Electronics Express. 16(5). 20190004–20190004. 8 indexed citations
6.
Sun, Shengyang, Hui Xu, Jiwei Li, Haijun Liu, & Qingjiang Li. (2019). Cascaded Neural Network for Memristor based Neuromorphic Computing. abs 1409 1556. 1–6. 2 indexed citations
7.
Li, Jiwei, Hui Xu, Shengyang Sun, et al.. (2019). Enhanced Spiking Neural Network with forgetting phenomenon based on electronic synaptic devices. Neurocomputing. 408. 21–30. 5 indexed citations
8.
Sun, Shengyang, Hui Xu, Jiwei Li, Qingjiang Li, & Haijun Liu. (2019). Cascaded Architecture for Memristor Crossbar Array Based Larger-Scale Neuromorphic Computing. IEEE Access. 7. 61679–61688. 14 indexed citations
9.
Sun, Shengyang, et al.. (2019). A memristor-based convolutional neural network with full parallelization architecture. IEICE Electronics Express. 16(3). 20181034–20181034. 10 indexed citations
10.
Sun, Shengyang, Hui Xu, Jiwei Li, et al.. (2019). Cases Study of Inputs Split Based Calibration Method for RRAM Crossbar. IEEE Access. 7. 141792–141800. 3 indexed citations
11.
Ba, Jimmy, et al.. (2019). Towards Characterizing the High-dimensional Bias of Kernel-based Particle Inference Algorithms. 3 indexed citations
12.
Tal, Gil, et al.. (2018). Motivations and Barriers Associated with the Adoption of Battery Electric Vehicles in Beijing: A Multinomial Logit Model Approach. eScholarship (California Digital Library). 3 indexed citations
13.
Sun, Shengyang, Guodong Zhang, Chaoqi Wang, et al.. (2018). Differentiable Compositional Kernel Learning for Gaussian Processes. International Conference on Machine Learning. 4828–4837. 7 indexed citations
14.
Shi, Jiaxin, Shengyang Sun, & Jun Zhu. (2018). A Spectral Approach to Gradient Estimation for Implicit Distributions.. International Conference on Machine Learning. 4644–4653. 3 indexed citations
15.
Sun, Shengyang, Jiwei Li, Zhiwei Li, et al.. (2018). Low-Consumption Neuromorphic Memristor Architecture Based on Convolutional Neural Networks. 1–6. 11 indexed citations
16.
Sun, Shengyang, Changyou Chen, & Lawrence Carin. (2017). Learning Structured Weight Uncertainty in Bayesian Neural Networks. International Conference on Artificial Intelligence and Statistics. 1283–1292. 26 indexed citations
17.
Zhang, Jiayi, Linglong Dai, Shengyang Sun, & Zhaocheng Wang. (2016). On the Spectral Efficiency of Massive MIMO Systems With Low-Resolution ADCs. IEEE Communications Letters. 20(5). 842–845. 192 indexed citations
18.
Li, Chunyan, Shengyang Sun, & Jifu Guo. (2015). Evaluation the Impacts of Bicycle-Sharing Systems on Carbon Emission Reductions ‒ Empirical Study in Beijing. Transportation Research Board 94th Annual MeetingTransportation Research Board. 1 indexed citations
19.
Hunt, John Douglas, et al.. (2012). Short Distance Personal Travel Model (SDPTM) in the California Statewide Transportation Demand Model. Transportation Research Board 91st Annual MeetingTransportation Research Board. 2 indexed citations
20.
Zhou, Yue, Jiangyan Wang, Di Huang, & Shengyang Sun. (2009). Pedestrian Simulation Modeling for World Expo 2010 Shanghai. Journal of Transportation Systems Engineering and Information Technology. 9(2). 141–146. 18 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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