Shangyu Chen

444 total citations
5 papers, 54 citations indexed

About

Shangyu Chen is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Shangyu Chen has authored 5 papers receiving a total of 54 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 2 papers in Hardware and Architecture and 2 papers in Artificial Intelligence. Recurrent topics in Shangyu Chen's work include Advanced Neural Network Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Shangyu Chen is often cited by papers focused on Advanced Neural Network Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Shangyu Chen collaborates with scholars based in Singapore, China and Belgium. Shangyu Chen's co-authors include Sinno Jialin Pan, Wenya Wang, Eun-Young Kang and Ke Liu and has published in prestigious journals such as Neural Information Processing Systems, Repository of the University of Namur and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Shangyu Chen

5 papers receiving 53 citations

Peers

Countries citing papers authored by Shangyu Chen

Since Specialization

Citations

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

Fields of papers citing papers by Shangyu Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shangyu Chen

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

All Works

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5 of 5 papers shown

#	Work	Indexed citations
1	Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning 2020 ·Neural Information Processing Systems ·(unknown), Shangyu Chen, Sinno Jialin Pan	7
2	MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization 2019 ·Neural Information Processing Systems ·Shangyu Chen, Wenya Wang, Sinno Jialin Pan	13
3	Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data 2019 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Shangyu Chen, Wenya Wang, Sinno Jialin Pan	21
4	Cooperative Pruning in Cross-Domain Deep Neural Network Compression 2019 ·Shangyu Chen, Wenya Wang, Sinno Jialin Pan	9
5	Statistical analysis of energy-aware real-time automotive systems in EAST-ADL/Stateflow 2016 ·Repository of the University of Namur ·Eun-Young Kang, (unknown), Ke Liu, Shangyu Chen	4

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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