Guolin Ke
- Artificial Intelligence top 0.5%
- Machine Learning and Data Classification 5
- Topic Modeling 4
- Natural Language Processing Techniques 3
- Health Information Management top 0.5%
- Environmental Engineering top 2%
- Signal Processing top 2%
- Health Informatics top 2%
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- Machine Learning in Materials Science 6
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- Computational Drug Discovery Methods 3
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- Advanced Neural Network Applications 3
- Graph Theory and Algorithms 2
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- Protein Structure and Dynamics 2
- Journals
- Nature Communications (2 papers)Advanced Optical Materials (2 papers)National Science Review (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Guolin Ke
20 papers receiving 7.8k citations
Hit Papers
Peers
Comparison fields: 5 of 218
- Artificial Intelligence 2.5k
- Health Information Management 229
- Environmental Engineering 624
- Signal Processing 441
- Health Informatics 55
Countries citing papers authored by Guolin Ke
This map shows the geographic impact of Guolin Ke'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 Guolin Ke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guolin Ke more than expected).
Fields of papers citing papers by Guolin Ke
This network shows the impact of papers produced by Guolin Ke. 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 Guolin Ke. The network helps show where Guolin Ke may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guolin Ke, 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 | 2025 | 9 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 17 | |
| 4 | 2024 | 15 | |
| 5 | 2024 | 22 | |
| 6 | 2024 | 62 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 1 | |
| 9 | 2023 | 0 | |
| 10 | 2023 | 8 | |
| 11 | Rethinking Positional Encoding in Language Pre-training | 2021 | 82 |
| 12 | Do Transformers Really Perform Badly for Graph Representationbreakdown → | 2021 | 248 |
| 13 | 2021 | 33 | |
| 14 | Light Gradient Boosting Machine [R package lightgbm version 3.2.0] | 2021 | 1 |
| 15 | Taking Notes on the Fly Helps Language Pre-Training | 2021 | 9 |
| 16 | Deep Subdomain Adaptation Network for Image Classificationbreakdown → | 2020 | 843 |
| 17 | 2020 | 2 | |
| 18 | 2019 | 86 | |
| 19 | A Highly Efficient Gradient Boosting Decision Tree | 2017 | 46 |
| 20 | 2016 | 52 |
About Guolin Ke
Guolin Ke is a scholar working on Structural Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Surfaces, Coatings and Films and Computational Theory and Mathematics, having authored 22 papers that have together received 8.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (6 papers), Machine Learning and Data Classification (5 papers), Topic Modeling (4 papers), Computational Drug Discovery Methods (3 papers), Advanced Neural Network Applications (3 papers), Natural Language Processing Techniques (3 papers), Protein Structure and Dynamics (2 papers) and Graph Theory and Algorithms (2 papers). The work is most often cited by research in Artificial Intelligence (2.5k citations), Health Information Management (229 citations), Environmental Engineering (624 citations), Signal Processing (441 citations) and Health Informatics (55 citations). Guolin Ke has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Tie‐Yan Liu, Qi Meng, Qiwei Ye, Weidong Ma, Taifeng Wang, Wei Chen, Thomas Finley, Yongchun Zhu, Jiang Bian and Jindong Wang. Their work appears in journals such as Nature Communications, Advanced Optical Materials, National Science Review, Advanced Science and IEEE Transactions on Neural Networks and Learning 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.