Shao-Yuan Li

722 total citations · 1 hit paper
19 papers, 517 citations indexed

About

Shao-Yuan Li is a scholar working on Artificial Intelligence, Computer Science Applications and Information Systems. According to data from OpenAlex, Shao-Yuan Li has authored 19 papers receiving a total of 517 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 6 papers in Computer Science Applications and 3 papers in Information Systems. Recurrent topics in Shao-Yuan Li's work include Text and Document Classification Technologies (7 papers), Mobile Crowdsensing and Crowdsourcing (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Shao-Yuan Li is often cited by papers focused on Text and Document Classification Technologies (7 papers), Mobile Crowdsensing and Crowdsourcing (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Shao-Yuan Li collaborates with scholars based in China, United States and Switzerland. Shao-Yuan Li's co-authors include Zhi‐Hua Zhou, Yuan Jiang, Sheng-Jun Huang, Songcan Chen, Yuan Jiang, Nitesh V. Chawla, Jie Tang, Daizong Ding, Mi Zhang and Guoxiang Li and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Knowledge and Data Engineering and Neural Networks.

In The Last Decade

Shao-Yuan Li

16 papers receiving 512 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Partial Multi-View Clustering

2014 327 citations Shao-Yuan Li, Yuan Jiang et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Shao-Yuan Li China	8	331	299	70	67	51	19	517
Meiyu Liang China	11	215 0.6×	198 0.7×	58 0.8×	33 0.5×	12 0.2×	63	384
Weixiang Shao United States	10	242 0.7×	154 0.5×	134 1.9×	31 0.5×	3 0.1×	15	383
Chao Lan United States	11	151 0.5×	148 0.5×	26 0.4×	44 0.7×	4 0.1×	43	325
Kamran Ghasedi Dizaji United States	6	311 0.9×	300 1.0×	13 0.2×	28 0.4×	14 0.3×	9	525
Tiansheng Yao United States	8	248 0.7×	126 0.4×	232 3.3×	33 0.5×	4 0.1×	8	363
Weitong Zhang China	15	246 0.7×	150 0.5×	29 0.4×	70 1.0×	5 0.1×	48	527
Guangnan Ye United States	14	292 0.9×	788 2.6×	16 0.2×	42 0.6×	5 0.1×	26	876
Rong Xiao China	11	225 0.7×	220 0.7×	92 1.3×	19 0.3×	3 0.1×	19	420
Wenkui Ding China	8	151 0.5×	157 0.5×	86 1.2×	7 0.1×	19 0.4×	9	368

Countries citing papers authored by Shao-Yuan Li

Since Specialization

Citations

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

Fields of papers citing papers by Shao-Yuan Li

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shao-Yuan 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 Shao-Yuan Li. The network helps show where Shao-Yuan Li may publish in the future.

Co-authorship network of co-authors of Shao-Yuan Li

This figure shows the co-authorship network connecting the top 25 collaborators of Shao-Yuan Li. A scholar is included among the top collaborators of Shao-Yuan 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 Shao-Yuan Li. Shao-Yuan Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

Sun, Yue, Dong Liang, Shao-Yuan Li, Songcan Chen, & Sheng-Jun Huang. (2025). Handling Noisy Annotation for Remote Sensing Semantic Segmentation via Boundary-Aware Knowledge Distillation. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–20.

Li, Shao-Yuan, et al.. (2024). UNM: A Universal Approach for Noisy Multi-Label Learning. IEEE Transactions on Knowledge and Data Engineering. 36(9). 4968–4980. 1 indexed citations

Pellegrini, Lorenzo, Zixuan Zhao, Fangfang Xia, et al.. (2024). Continual learning in the presence of repetition. Neural Networks. 183. 106920–106920.

Li, Shao-Yuan, et al.. (2024). KD-Crowd: a knowledge distillation framework for learning from crowds. Frontiers of Computer Science. 19(1). 1 indexed citations

Xie, Ming-Kun, et al.. (2024). Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(14). 15438–15446. 5 indexed citations

Li, Shao-Yuan, et al.. (2024). Robust domain adaptation with noisy and shifted label distribution. Frontiers of Computer Science. 19(3). 2 indexed citations

Zhu, Xiaolin, Xiang Chen, Pingze Zhang, et al.. (2023). Research on Intelligent Identification and Grading of Nonmetallic Inclusions in Steels Based on Deep Learning. Micromachines. 14(2). 482–482. 5 indexed citations

Chen, Jiayao & Shao-Yuan Li. (2023). Class-aware Learning for Imbalanced Multi-Label Classification. 903–907.

Li, Shao-Yuan, et al.. (2022). Deep Generative Crowdsourcing Learning with Worker Correlation Utilization. 12(2). 213–230. 1 indexed citations

10.

Shi, Ye, Shao-Yuan Li, & Sheng-Jun Huang. (2022). Learning from crowds with sparse and imbalanced annotations. Machine Learning. 112(6). 1823–1845. 4 indexed citations

11.

Li, Shao-Yuan, Ye Shi, Sheng-Jun Huang, & Songcan Chen. (2022). Improving deep label noise learning with dual active label correction. Machine Learning. 111(3). 1103–1124. 4 indexed citations

12.

Li, Shao-Yuan, Sheng-Jun Huang, & Songcan Chen. (2021). Crowdsourcing aggregation with deep Bayesian learning. Science China Information Sciences. 64(3). 30 indexed citations

13.

Li, Shao-Yuan, et al.. (2020). Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4957–4964. 15 indexed citations

14.

Huang, Sheng-Jun, et al.. (2020). Incremental Multi-Label Learning with Active Queries. Journal of Computer Science and Technology. 35(2). 234–246. 18 indexed citations

15.

Li, Shao-Yuan, Yuan Jiang, Nitesh V. Chawla, & Zhi‐Hua Zhou. (2018). Multi-Label Learning from Crowds. IEEE Transactions on Knowledge and Data Engineering. 31(7). 1369–1382. 35 indexed citations

16.

Ding, Daizong, et al.. (2017). BayDNN. 1479–1488. 38 indexed citations

17.

Wang, Wei, Xiangyu Guo, Shao-Yuan Li, Yuan Jiang, & Zhi‐Hua Zhou. (2017). Obtaining High-Quality Label by Distinguishing between Easy and Hard Items in Crowdsourcing. 2964–2970. 15 indexed citations

18.

Li, Shao-Yuan, Yuan Jiang, & Zhi‐Hua Zhou. (2014). Partial Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 327 indexed citations breakdown →

19.

Wu, Bin, Bo Huang, Ying Chen, et al.. (2013). Upregulated expression of Tim-3 involved in the process of toxoplasmic encephalitis in mouse model. Parasitology Research. 112(7). 2511–2521. 16 indexed citations

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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