Qiwei Ye

9.8k citations
8 papers · 6.7k indexed · 1 hit paper · h-index 5

Qiwei Ye

6 papers receiving 6.5k citations

Hit Papers

LightGBM: A Highly Efficient Gradient Boosting Decision Tree6.5k20172026202020232.0k4.0k6.0k

Peers

Qiwei Ye
Comparison fields: 5 of 215
  • Artificial Intelligence 1.8k
  • Health Information Management 214
  • Environmental Engineering 603
  • Health Informatics 50
  • Signal Processing 361
Replace Qi Meng with:
Qi Meng China
Weidong Ma China
Taifeng Wang China
Guolin Ke China
Zigang Lu China
Thomas Finley United States
Pierre Geurts Belgium
Xue Li China
Alois Knoll Germany
James Bergstra Canada
Qiwei Ye relative to Qi Meng China Qi Meng's profile →
Citations per field
00.5×
Qi Meng · 1×
Citations per year

Countries citing papers authored by Qiwei Ye

Since Specialization
Citations

This map shows the geographic impact of Qiwei Ye'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 Qiwei Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qiwei Ye more than expected).

Fields of papers citing papers by Qiwei Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qiwei Ye. 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 Qiwei Ye. The network helps show where Qiwei Ye may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Qiwei Ye, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Qiwei Ye Line = papers co-authored together Qiwei Ye links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 202413
2 20240
3
Light Gradient Boosting Machine [R package lightgbm version 3.2.0]
20211
4 20210
5
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space.
20186
6
LightGBM: A Highly Efficient Gradient Boosting Decision Treebreakdown →
20176473
7 201652
8 2004136

About Qiwei Ye

Qiwei Ye is a scholar working on Health Information Management, Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Polymers and Plastics, having authored 8 papers that have together received 6.7k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (3 papers), Advanced Neural Network Applications (2 papers), Machine Learning and Data Classification (2 papers), Artificial Intelligence in Healthcare (1 paper), Protein Structure and Dynamics (1 paper), Neural Networks and Applications (1 paper), Machine Learning in Materials Science (1 paper) and Aerogels and thermal insulation (1 paper). The work is most often cited by research in Artificial Intelligence (1.8k citations), Health Information Management (214 citations), Environmental Engineering (603 citations), Health Informatics (50 citations) and Signal Processing (361 citations). Qiwei Ye has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Guolin Ke, Qi Meng, Thomas Finley, Tie‐Yan Liu, Taifeng Wang, Weidong Ma, Wei Chen, Dingcai Wu, Wei Xu and Shuting Zhang. Their work appears in journals such as Carbon, Nature Machine Intelligence, HAL (Le Centre pour la Communication Scientifique Directe), arXiv (Cornell University) and International Conference on Learning Representations.

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.

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