Kun He

5.0k total citations · 2 hit papers
135 papers, 2.5k citations indexed

About

Kun He is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering. According to data from OpenAlex, Kun He has authored 135 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Artificial Intelligence, 32 papers in Computer Vision and Pattern Recognition and 31 papers in Industrial and Manufacturing Engineering. Recurrent topics in Kun He's work include Optimization and Packing Problems (24 papers), Adversarial Robustness in Machine Learning (22 papers) and Complex Network Analysis Techniques (18 papers). Kun He is often cited by papers focused on Optimization and Packing Problems (24 papers), Adversarial Robustness in Machine Learning (22 papers) and Complex Network Analysis Techniques (18 papers). Kun He collaborates with scholars based in China, United States and France. Kun He's co-authors include Xiaosen Wang, John E. Hopcroft, Yihe Deng, Shuhuai Ren, Wanxiang Che, David Bindel, Wenqi Huang, Wenqi Huang, Yixuan Li and Jingdong Wang and has published in prestigious journals such as PLoS ONE, Scientific Reports and European Journal of Operational Research.

In The Last Decade

Kun He

119 papers receiving 2.4k citations

Hit Papers

Generating Natural Language Adversarial Examples through ... 2019 2026 2021 2023 2019 2021 100 200 300
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. Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
    2019 363 citations Kun He et al. profile →
  2. Enhancing the Transferability of Adversarial Attacks through Variance Tuning
    2021 242 citations Xiaosen Wang, Kun He profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kun He China 26 1.3k 490 442 312 277 135 2.5k
Daniel Aloise Canada 17 561 0.4× 250 0.5× 261 0.6× 143 0.5× 228 0.8× 63 1.5k
S. S. Ravi United States 27 456 0.4× 233 0.5× 191 0.4× 258 0.8× 407 1.5× 125 2.6k
Daniel J. Rosenkrantz United States 27 1.1k 0.8× 194 0.4× 519 1.2× 135 0.4× 208 0.8× 128 3.3k
Simon Lin Taiwan 7 1.1k 0.8× 392 0.8× 1.6k 3.6× 65 0.2× 157 0.6× 22 2.6k
Amit Kumar India 26 435 0.3× 198 0.4× 209 0.5× 190 0.6× 267 1.0× 119 2.3k
Weimin Li China 25 749 0.6× 445 0.9× 98 0.2× 348 1.1× 150 0.5× 176 2.1k
Ran El‐Yaniv Israel 23 1.1k 0.9× 459 0.9× 342 0.8× 49 0.2× 189 0.7× 69 3.1k
Paul G. Spirakis Greece 26 469 0.4× 275 0.6× 131 0.3× 209 0.7× 133 0.5× 260 2.7k
Giuseppe F. Italiano Italy 26 794 0.6× 398 0.8× 85 0.2× 124 0.4× 373 1.3× 170 2.7k
Kamalika Chaudhuri United States 20 2.1k 1.6× 583 1.2× 67 0.2× 127 0.4× 164 0.6× 65 3.0k

Countries citing papers authored by Kun He

Since Specialization
Citations

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

Fields of papers citing papers by Kun He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kun He

This figure shows the co-authorship network connecting the top 25 collaborators of Kun He. A scholar is included among the top collaborators of Kun He 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 Kun He. Kun He 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.
Chen, Jinsong, et al.. (2025). GTPool: Graph Transformer Pooling With Diverse Sampling. IEEE Transactions on Big Data. 11(6). 3255–3267.
2.
3.
He, Kun, et al.. (2024). Error-feedback three-phase optimization to configurable convolutional echo state network for time series forecasting. Applied Soft Computing. 161. 111715–111715. 1 indexed citations
4.
He, Kun, et al.. (2024). Integrating multi-armed bandit with local search for MaxSAT. Artificial Intelligence. 338. 104242–104242.
5.
Chen, Jinsong, et al.. (2024). Neighborhood convolutional graph neural network. Knowledge-Based Systems. 295. 111861–111861. 18 indexed citations
6.
Liu, Hanpeng, et al.. (2024). Knowledge Distillation via Token-Level Relationship Graph Based on the Big Data Technologies. Big Data Research. 36. 100438–100438. 2 indexed citations
7.
He, Kun, et al.. (2024). An efficient solution space exploring and descent method for packing equal spheres in a sphere. Computers & Operations Research. 164. 106522–106522. 4 indexed citations
8.
Li, Bicheng, et al.. (2024). MCM-ViT:Mask-guided context-enhanced multi-scale transformer for fine-grained visual classification. Computers & Electrical Engineering. 122. 109888–109888.
9.
He, Kun, et al.. (2023). A Simulated Annealing approach for the Circle Bin Packing Problem with Rectangular Items. Computers & Industrial Engineering. 176. 109004–109004. 20 indexed citations
10.
He, Kun, et al.. (2023). Semantic Adversarial Attacks on Face Recognition Through Significant Attributes. International Journal of Computational Intelligence Systems. 16(1). 1 indexed citations
11.
Wang, Xiaosen, Hao Jin, Yichen Yang, & Kun He. (2021). Natural Language Adversarial Defense through Synonym Encoding. Uncertainty in Artificial Intelligence. 11 indexed citations
12.
Song, Chuanbiao, Kun He, Jiadong Lin, Liwei Wang, & John E. Hopcroft. (2020). Robust Local Features for Improving the Generalization of Adversarial Training. arXiv (Cornell University). 6 indexed citations
13.
He, Kun, et al.. (2018). A Novel Task-Duplication Based Clustering Algorithm for Heterogeneous Computing Environments. IEEE Transactions on Parallel and Distributed Systems. 30(1). 2–14. 43 indexed citations
14.
Song, Chuanbiao, Kun He, Liwei Wang, & John E. Hopcroft. (2018). Improving the Generalization of Adversarial Training with Domain Adaptation. International Conference on Learning Representations. 21 indexed citations
15.
Chen, Hu, Yi Zhang, Weihua Zhang, et al.. (2017). Learned Experts' Assessment-based Reconstruction Network ("LEARN") for Sparse-data CT.. arXiv (Cornell University). 3 indexed citations
16.
He, Kun, et al.. (2017). Understanding Deep Representations through Random Weights.. arXiv (Cornell University). 3 indexed citations
17.
He, Kun, Yan Wang, & John E. Hopcroft. (2016). A Powerful Generative Model Using Random Weights for the Deep Image Representation. Neural Information Processing Systems. 29. 631–639. 9 indexed citations
18.
Chen, Zengjing, Kun He, & Reg Kulperger. (2013). Risk Measures and Nonlinear Expectations. Journal of Mathematical Finance. 3(3). 383–391. 3 indexed citations
19.
He, Kun. (2010). Implementation of On-Demand Routing Protocol Based on LED Light Communication. Journal of Jilin University. 1 indexed citations
20.
He, Kun & Wenqi Huang. (2009). Solving the single-container loading problem by a fast heuristic method. Optimization methods & software. 25(2). 263–277. 7 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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