Shao‐Lun Huang
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering
- Electrical and Electronic Engineering
- Cognitive Neuroscience top 10%
- Co-authors
- Lizhong ZhengYang LiLin ZhangXiangxiang XuFei MaJian XuTian LanLinqi Song
- Topics
- Neural Networks and Applications (14 papers)Distributed Sensor Networks and Detection Algorithms (14 papers)Domain Adaptation and Few-Shot Learning (13 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsIEEE Transactions on Information Theory
- Partner nations
- ChinaUnited StatesTaiwan
In The Last Decade
Shao‐Lun Huang
104 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 116
- Artificial Intelligence 423
- Computer Vision and Pattern Recognition 250
- Biomedical Engineering 242
- Electrical and Electronic Engineering 203
- Cognitive Neuroscience 132
Countries citing papers authored by Shao‐Lun Huang
This map shows the geographic impact of Shao‐Lun Huang'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 Shao‐Lun Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shao‐Lun Huang more than expected).
Fields of papers citing papers by Shao‐Lun Huang
This network shows the impact of papers produced by Shao‐Lun Huang. 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 Shao‐Lun Huang. The network helps show where Shao‐Lun Huang may publish in the future.
Co-authorship network of co-authors of Shao‐Lun Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Shao‐Lun Huang. A scholar is included among the top collaborators of Shao‐Lun Huang 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 Shao‐Lun Huang. Shao‐Lun Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 4 | |
| 9 | 1 | |
| 10 | A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning | 9 |
| 11 | 5 | |
| 12 | Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback | 1 |
| 13 | 5 | |
| 14 | 53 | |
| 15 | 10 | |
| 16 | 2 | |
| 17 | Poster Abstract: Analysis and Evaluation of Driving Behavior Recognition Based on a 3-axis Accelerometer Using a Random Forest Approach | 5 |
| 18 | 12 | |
| 19 | 6 | |
| 20 | 5 |
About Shao‐Lun Huang
Shao‐Lun Huang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 117 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (14 papers), Distributed Sensor Networks and Detection Algorithms (14 papers) and Domain Adaptation and Few-Shot Learning (13 papers). The work is most often cited by research in Artificial Intelligence (423 citations), Computer Vision and Pattern Recognition (250 citations) and Signal Processing (104 citations). Shao‐Lun Huang has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Lizhong Zheng, Yang Li, Lin Zhang, Xiangxiang Xu, Fei Ma, Lin Zhang, Jian Xu, Tian Lan, Linqi Song and Zihan Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Information Theory.
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.