Lijun Wu

3.4k total citations · 1 hit paper
63 papers, 1.2k citations indexed

About

Lijun Wu is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Lijun Wu has authored 63 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 14 papers in Molecular Biology and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Lijun Wu's work include Natural Language Processing Techniques (24 papers), Topic Modeling (23 papers) and Multimodal Machine Learning Applications (9 papers). Lijun Wu is often cited by papers focused on Natural Language Processing Techniques (24 papers), Topic Modeling (23 papers) and Multimodal Machine Learning Applications (9 papers). Lijun Wu collaborates with scholars based in China, United States and United Kingdom. Lijun Wu's co-authors include Tao Qin, Tie‐Yan Liu, Yuan Liu, Yingce Xia, Fei Tian, Jianhuang Lai, Jinhua Zhu, Tie‐Yan Liu, Shufang Xie and Bo Shao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Lijun Wu

59 papers receiving 1.2k citations

Hit Papers

Recent advances of cathode materials for zinc-ion hybrid ... 2023 2026 2024 2025 2023 40 80 120
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.

  1. Recent advances of cathode materials for zinc-ion hybrid capacitors
    2023 138 citations Lijun Wu et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lijun Wu China 20 611 257 150 116 112 63 1.2k
Luo Wang China 17 683 1.1× 508 2.0× 192 1.3× 30 0.3× 23 0.2× 55 1.9k
Yunhao Zhang China 16 82 0.1× 134 0.5× 80 0.5× 68 0.6× 53 0.5× 78 793
Weijie Zhao China 17 187 0.3× 144 0.6× 74 0.5× 29 0.3× 17 0.2× 80 1.1k
Lu Sun China 16 173 0.3× 133 0.5× 57 0.4× 47 0.4× 26 0.2× 59 837
Xupeng Wang China 15 212 0.3× 126 0.5× 141 0.9× 27 0.2× 14 0.1× 77 772
Eun Jung Kim United States 21 103 0.2× 34 0.1× 485 3.2× 161 1.4× 70 0.6× 118 1.5k
Yannan Chen China 12 127 0.2× 130 0.5× 160 1.1× 30 0.3× 26 0.2× 51 761

Countries citing papers authored by Lijun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Lijun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lijun Wu

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

All Works

20 of 20 papers shown
1.
2.
He, Liang, Pan Deng, Z. G. Zhao, et al.. (2025). Accelerating protein engineering with fitness landscape modelling and reinforcement learning. Nature Machine Intelligence. 7(9). 1446–1460. 3 indexed citations
3.
Li, Juntao, Xiaobo Liang, Lijun Wu, et al.. (2024). Randomness Regularization With Simple Consistency Training for Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(8). 5763–5778. 4 indexed citations
4.
Gao, K. Y., Lijun Wu, Jinhua Zhu, et al.. (2023). Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design. 506–517. 7 indexed citations
5.
Wu, Lijun, et al.. (2023). Experimental Investigation on Evaporator Surface Modification for Hydrophobicity and Frost Resistance. Materials Science. 29(1). 48–57. 4 indexed citations
6.
Zhu, Jinhua, Yingce Xia, Lijun Wu, et al.. (2023). Dual-view Molecular Pre-training. 3615–3627. 28 indexed citations
7.
Wu, Lijun, et al.. (2023). Ecological Risk Assessment of Heavy Metals in Sediment from Beijing-Hangzhou Grand Canal. SHILAP Revista de lepidopterología. 393. 2028–2028. 1 indexed citations
8.
Xu, Ruiyang, Lijun Wu, Juntao Li, et al.. (2023). AMOM: Adaptive Masking over Masking for Conditional Masked Language Model. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13789–13797. 4 indexed citations
9.
Liang, Xiaobo, et al.. (2023). Enhancing Low-Resource NLP by Consistency Training With Data and Model Perturbations. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 189–199. 5 indexed citations
10.
Wu, Lijun, Junliang Guo, Juntao Li, et al.. (2023). A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(10). 11407–11427. 45 indexed citations
11.
Liang, Xiaobo, et al.. (2023). CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation. 3901–3913. 5 indexed citations
12.
Wu, Lijun, Jinhua Zhu, Yingce Xia, et al.. (2023). Breaking the barriers of data scarcity in drug–target affinity prediction. Briefings in Bioinformatics. 24(6). 20 indexed citations
13.
Hou, Yutai, Yingce Xia, Lijun Wu, et al.. (2022). Discovering drug–target interaction knowledge from biomedical literature. Bioinformatics. 38(22). 5100–5107. 11 indexed citations
14.
Liang, Xiaobo, Lijun Wu, Juntao Li, et al.. (2022). Multi-Teacher Distillation With Single Model for Neural Machine Translation. IEEE/ACM Transactions on Audio Speech and Language Processing. 30. 992–1002. 14 indexed citations
15.
Wu, Zhen, Lijun Wu, Qi Meng, et al.. (2021). UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost. 3865–3878. 18 indexed citations
16.
Wu, Lijun, Xu Tan, Tao Qin, Jianhuang Lai, & Tie‐Yan Liu. (2019). Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation. IEEE/ACM Transactions on Audio Speech and Language Processing. 27(12). 1868–1879. 2 indexed citations
17.
Wu, Lijun, Fei Tian, Tao Qin, Jianhuang Lai, & Tie‐Yan Liu. (2018). A Study of Reinforcement Learning for Neural Machine Translation. 3612–3621. 82 indexed citations
18.
Wu, Lijun, Fei Tian, Yingce Xia, et al.. (2018). Learning to Teach with Dynamic Loss Functions. Neural Information Processing Systems. 31. 6466–6477. 16 indexed citations
19.
Wu, Lijun, Yingce Xia, Fei Tian, et al.. (2018). Adversarial Neural Machine Translation. Asian Conference on Machine Learning. 534–549. 35 indexed citations
20.
Xia, Yingce, Fei Tian, Lijun Wu, et al.. (2017). Deliberation Networks: Sequence Generation Beyond One-Pass Decoding. Neural Information Processing Systems. 30. 1784–1794. 117 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Luo Wang Breakdown of academic impact, for papers by Yunhao Zhang Breakdown of academic impact, for papers by Weijie Zhao Breakdown of academic impact, for papers by Lu Sun Breakdown of academic impact, for papers by Xupeng Wang Breakdown of academic impact, for papers by Eun Jung Kim Breakdown of academic impact, for papers by Yannan Chen
Rankless by CCL
2026