Liang Ding

3.7k total citations · 1 hit paper
166 papers, 2.0k citations indexed

About

Liang Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Liang Ding has authored 166 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Artificial Intelligence, 34 papers in Computer Vision and Pattern Recognition and 30 papers in Electrical and Electronic Engineering. Recurrent topics in Liang Ding's work include Topic Modeling (48 papers), Natural Language Processing Techniques (39 papers) and Multimodal Machine Learning Applications (20 papers). Liang Ding is often cited by papers focused on Topic Modeling (48 papers), Natural Language Processing Techniques (39 papers) and Multimodal Machine Learning Applications (20 papers). Liang Ding collaborates with scholars based in China, Australia and Singapore. Liang Ding's co-authors include Dacheng Tao, Ling‐Yu Duan, Li Shen, Lin Zhang, Qihuang Zhong, Xuebo Liu, Bo Du, Juhua Liu, Longyue Wang and Han Zhou and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Biomaterials.

In The Last Decade

Liang Ding

148 papers receiving 1.9k 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.

Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning

2022 210 citations Liang Ding, Dacheng Tao et al. profile →

Peers

Liang Ding

AI

EEE

MC

CVPR

RESE

Duo Wang China

Yuqing Wu China

Jiandong Wang China

Bei Chen China

Tianyi Xu China

Wenhua Wang China

Vincent Lee United States

Kaisheng Yao China

Zhengfeng Huang China

Weiping Wang China

Duo Wang China

Liang Ding

345 ×0.4

AI

480 ×0.9

EEE

455 ×1.3

MC

317 ×1.1

CVPR

442 ×1.6

RESE

Citations per year, relative to Liang Ding Liang Ding (= 1×) peers Duo Wang

Countries citing papers authored by Liang Ding

Since Specialization

Citations

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

Fields of papers citing papers by Liang Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Ding. A scholar is included among the top collaborators of Liang Ding 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 Liang Ding. Liang Ding 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:

20 of 20 papers shown

1.

Jiang, Jie, Yibing Zhan, Chong Zhang, et al.. (2025). Hypnos: A domain-specific large language model for anesthesiology. Neurocomputing. 624. 129389–129389. 5 indexed citations

2.

Peng, Yong, et al.. (2025). Deep Model Fusion: A Survey. IEEE Transactions on Neural Networks and Learning Systems. PP. 1–17.

3.

Meng, Longhui, et al.. (2024). Enhancing curvature prediction in flexible printed circuits: A computational approach integrating analytical models with finite element simulations. Alexandria Engineering Journal. 107. 547–558. 1 indexed citations

4.

Liu, Chuang, Yibing Zhan, Liang Ding, et al.. (2024). Exploring sparsity in graph transformers. Neural Networks. 174. 106265–106265. 3 indexed citations

5.

Ding, Liang, et al.. (2024). Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models. 8801–8816. 4 indexed citations

6.

Zhong, Qihuang, Liang Ding, Juhua Liu, Bo Du, & Dacheng Tao. (2024). ROSE Doesn’t Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding. 13721–13736. 2 indexed citations

7.

Liu, Xuebo, et al.. (2024). Speech Sense Disambiguation: Tackling Homophone Ambiguity in End-to-End Speech Translation. 8020–8035. 1 indexed citations

8.

Li, Mengting, Ziying Yang, Shiyu Cao, et al.. (2024). An integrated “Engage & Evasion” approach for mononuclear phagocyte system escape and efficient extracellular vesicle therapy. Journal of Nanobiotechnology. 22(1). 770–770. 4 indexed citations

9.

Zhong, Qihuang, Liang Ding, Li Shen, et al.. (2024). Revisiting Knowledge Distillation for Autoregressive Language Models. 10900–10913. 4 indexed citations

10.

Ding, Liang, et al.. (2024). Research on the application of nuclear power-molten salt heat storage integration system technology. 468–472.

11.

Ding, Liang, Hao Xu, Min Zhang, et al.. (2024). Correction to “Thermoresponsive Polymeric Nanoparticles as Efficient Pickering Interfacial Catalysts for Selective Oxidation of Sulfides”. ACS Applied Polymer Materials. 6(7). 4349–4349. 1 indexed citations

12.

Shen, Li, et al.. (2024). On Efficient Training of Large-Scale Deep Learning Models. ACM Computing Surveys. 57(3). 1–36. 9 indexed citations

13.

Ding, Liang, et al.. (2023). Numerical investigation of unsteady film cooling on turbine blade squealer tip with pressure side coolant. International Communications in Heat and Mass Transfer. 143. 106720–106720. 12 indexed citations

14.

Ding, Liang, et al.. (2023). PAD-Net: An Efficient Framework for Dynamic Networks. 14354–14366. 1 indexed citations

15.

Wu, Han, et al.. (2023). Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts. CityU Scholars. 13128–13141. 1 indexed citations

16.

Peng, Keqin, Liang Ding, Qihuang Zhong, et al.. (2023). Towards Making the Most of ChatGPT for Machine Translation. 5622–5633. 37 indexed citations

17.

Ding, Liang, et al.. (2023). Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training. 10932–10940. 7 indexed citations

18.

Ding, Liang, Shuhan Qi, Meng Fang, et al.. (2023). Dynamic Contrastive Distillation for Image-Text Retrieval. IEEE Transactions on Multimedia. 25. 8383–8395. 23 indexed citations

19.

Ding, Liang, et al.. (2023). Parameter-Efficient and Student-Friendly Knowledge Distillation. IEEE Transactions on Multimedia. 26. 4230–4241. 33 indexed citations

20.

Nagarajan, Radhakrishnan, et al.. (2022). 2.5D Heterogeneous Integration for Silicon Photonics Engines in Optical Transceivers. IEEE Journal of Selected Topics in Quantum Electronics. 1–9. 22 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