Jiashuai Shi
- Artificial Intelligence top 10%
- Machine Learning and Data Classification 4
- Machine Learning and ELM 1
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- Advanced Image and Video Retrieval Techniques 3
- Face and Expression Recognition 2
- Advanced Neural Network Applications 1
- Graph Theory and Algorithms 1
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- CCD and CMOS Imaging Sensors 1
- Advanced Wireless Communication Technologies 1
- Co-authors
- Zeyi WenBingsheng HeQinbin LiKotagiri RamamohanaraoWei LiangShohei ShimizuKevin I‐Kai WangZheng Yan
In The Last Decade
Jiashuai Shi
7 papers receiving 332 citations
Hit Papers
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 159
- Computational Mathematics 2
- Computer Vision and Pattern Recognition 63
- Computer Networks and Communications 70
- Signal Processing 26
Countries citing papers authored by Jiashuai Shi
This map shows the geographic impact of Jiashuai Shi'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 Jiashuai Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiashuai Shi more than expected).
Fields of papers citing papers by Jiashuai Shi
This network shows the impact of papers produced by Jiashuai Shi. 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 Jiashuai Shi. The network helps show where Jiashuai Shi may publish in the future.
Co-authorship network
The 13 scholars most cited alongside Jiashuai Shi, 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 | Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networksbreakdown → | 2023 | 119 |
| 2 | ThunderGBM: Fast GBDTs and Random Forests on GPUs | 2020 | 20 |
| 3 | 2019 | 39 | |
| 4 | ThunderSVM: A Fast SVM Library on GPUs and CPUs | 2018 | 118 |
| 5 | 2018 | 8 | |
| 6 | 2018 | 3 | |
| 7 | 2018 | 32 |
About Jiashuai Shi
Jiashuai Shi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Electrical and Electronic Engineering and Infectious Diseases, having authored 7 papers that have together received 339 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Face and Expression Recognition (2 papers), Advanced Neural Network Applications (1 paper), CCD and CMOS Imaging Sensors (1 paper), Graph Theory and Algorithms (1 paper), Advanced Wireless Communication Technologies (1 paper) and Machine Learning and ELM (1 paper). The work is most often cited by research in Artificial Intelligence (159 citations), Computational Mathematics (2 citations), Computer Vision and Pattern Recognition (63 citations), Computer Networks and Communications (70 citations) and Signal Processing (26 citations). Jiashuai Shi has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Zeyi Wen, Bingsheng He, Qinbin Li, Kotagiri Ramamohanarao, Wei Liang, Shohei Shimizu, Kevin I‐Kai Wang, Zheng Yan, Laurence T. Yang and Xiaokang Zhou. Their work appears in journals such as IEEE Journal on Selected Areas in Communications, Journal of Machine Learning Research, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Knowledge and Data Engineering and Proceedings of the AAAI Conference on Artificial Intelligence.
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.