Zhiqiang Que
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Hardware and Architecture top 10%
- Control and Systems Engineering
- Co-authors
- Wayne LukHongxiang FanXinyu NiuShuanglong LiuMartin FeriancYongxin ZhuHo-Cheung NgHiroki Nakahara
- Topics
- Advanced Neural Network Applications (19 papers)Adversarial Robustness in Machine Learning (8 papers)Anomaly Detection Techniques and Applications (6 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Parallel and Distributed SystemsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Partner nations
- United KingdomChinaUnited States
In The Last Decade
Zhiqiang Que
49 papers receiving 492 citations
Peers
Comparison fields: 5 of 84
- Computer Vision and Pattern Recognition 238
- Artificial Intelligence 206
- Electrical and Electronic Engineering 143
- Hardware and Architecture 71
- Control and Systems Engineering 30
Countries citing papers authored by Zhiqiang Que
This map shows the geographic impact of Zhiqiang Que'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 Zhiqiang Que with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhiqiang Que more than expected).
Fields of papers citing papers by Zhiqiang Que
This network shows the impact of papers produced by Zhiqiang Que. 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 Zhiqiang Que. The network helps show where Zhiqiang Que may publish in the future.
Co-authorship network of co-authors of Zhiqiang Que
This figure shows the co-authorship network connecting the top 25 collaborators of Zhiqiang Que. A scholar is included among the top collaborators of Zhiqiang Que 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 Zhiqiang Que. Zhiqiang Que is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 15 | |
| 7 | 6 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 4 | |
| 11 | 7 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 7 | |
| 15 | 4 | |
| 16 | 4 | |
| 17 | 26 | |
| 18 | 7 | |
| 19 | Optimizing Bayesian Recurrent Neural Networks on an FPGA-based
\n Accelerator | 5 |
| 20 | 39 |
About Zhiqiang Que
Zhiqiang Que is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Artificial Intelligence, having authored 57 papers that have together received 504 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (19 papers), Adversarial Robustness in Machine Learning (8 papers) and Anomaly Detection Techniques and Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (238 citations), Hardware and Architecture (71 citations) and Artificial Intelligence (206 citations). Zhiqiang Que has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Wayne Luk, Hongxiang Fan, Xinyu Niu, Shuanglong Liu, Martin Ferianc, Yongxin Zhu, Ho-Cheung Ng, Hiroki Nakahara, Yuefeng Song and Сhen Liu. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
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.