Yuening Li
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Statistical and Nonlinear Physics top 10%
- Signal Processing
- Topics
- Anomaly Detection Techniques and Applications (7 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Recommender Systems and Techniques (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionStatistical and Nonlinear Physics
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsHumanities and Social Sciences CommunicationsPolyU Institutional Research Archive (Hong Kong Polytechnic University)
- Partner nations
- United StatesHong KongChina
In The Last Decade
Yuening Li
16 papers receiving 332 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 256
- Computer Vision and Pattern Recognition 117
- Computer Networks and Communications 71
- Statistical and Nonlinear Physics 63
- Signal Processing 43
Countries citing papers authored by Yuening Li
This map shows the geographic impact of Yuening Li'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 Yuening Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuening Li more than expected).
Fields of papers citing papers by Yuening Li
This network shows the impact of papers produced by Yuening Li. 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 Yuening Li. The network helps show where Yuening Li may publish in the future.
Co-authorship network of co-authors of Yuening Li
This figure shows the co-authorship network connecting the top 25 collaborators of Yuening Li. A scholar is included among the top collaborators of Yuening Li 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 Yuening Li. Yuening Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 5 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 5 | |
| 7 | 15 | |
| 8 | 1 | |
| 9 | 33 | |
| 10 | 12 | |
| 11 | 16 | |
| 12 | Towards deeper graph neural networks with differentiable group normalization | 6 |
| 13 | 23 | |
| 14 | 60 | |
| 15 | Towards Generalizable Forgery Detection with Locality-aware AutoEncoder. | 21 |
| 16 | 65 | |
| 17 | 59 | |
| 18 | 13 |
About Yuening Li
Yuening Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and General Social Sciences, having authored 18 papers that have together received 341 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (7 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Recommender Systems and Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (256 citations), Computer Vision and Pattern Recognition (117 citations) and Statistical and Nonlinear Physics (63 citations). Yuening Li has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include Xia Hu, Mengnan Du, Xiao Huang, Na Zou, Qingquan Song, Jundong Li, Daochen Zha, Zirui Liu, Haifeng Chen and Zhengzhang Chen. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Humanities and Social Sciences Communications and PolyU Institutional Research Archive (Hong Kong Polytechnic University).
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.