Jun Huan

2.4k total citations
54 papers, 1.3k citations indexed

About

Jun Huan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Jun Huan has authored 54 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 24 papers in Computer Vision and Pattern Recognition and 10 papers in Molecular Biology. Recurrent topics in Jun Huan's work include Domain Adaptation and Few-Shot Learning (19 papers), Machine Learning and ELM (8 papers) and Sparse and Compressive Sensing Techniques (7 papers). Jun Huan is often cited by papers focused on Domain Adaptation and Few-Shot Learning (19 papers), Machine Learning and ELM (8 papers) and Sparse and Compressive Sensing Techniques (7 papers). Jun Huan collaborates with scholars based in United States, China and Singapore. Jun Huan's co-authors include Jan F. Prins, Wei Wang, Brian Quanz, Jiong Yang, Jintao Zhang, Haoyi Xiong, Jack Snoeyink, Tianyang Wang, Deepak Bandyopadhyay and Alexander Tropsha and has published in prestigious journals such as IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.

In The Last Decade

Jun Huan

53 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jun Huan United States	17	599	553	432	271	219	54	1.3k
Negar Kiyavash United States	17	763 1.3×	472 0.9×	304 0.7×	487 1.8×	178 0.8×	111	1.5k
Mohammad Hossein Moattar Iran	17	536 0.9×	507 0.9×	139 0.3×	174 0.6×	244 1.1×	70	1.2k
M. Vento Italy	13	745 1.2×	909 1.6×	210 0.5×	403 1.5×	168 0.8×	24	1.7k
Sattar B. Sadkhan Iraq	16	425 0.7×	487 0.9×	164 0.4×	213 0.8×	90 0.4×	139	1.2k
Sajjad Shaukat Jamal Saudi Arabia	26	971 1.6×	1.2k 2.1×	330 0.8×	245 0.9×	130 0.6×	120	2.2k
L.P. Cordella Italy	17	739 1.2×	1.2k 2.1×	206 0.5×	399 1.5×	159 0.7×	53	1.9k
Kaspar Riesen Switzerland	19	747 1.2×	945 1.7×	132 0.3×	232 0.9×	103 0.5×	57	1.3k
Lantao Yu United States	9	930 1.6×	664 1.2×	230 0.5×	219 0.8×	63 0.3×	27	1.6k
Subramanyam Mallela United States	8	1.0k 1.7×	477 0.9×	451 1.0×	245 0.9×	184 0.8×	9	1.5k
Yuqiang Guan United States	8	1.1k 1.8×	806 1.5×	177 0.4×	198 0.7×	201 0.9×	9	1.8k

Countries citing papers authored by Jun Huan

Since Specialization

Citations

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

Fields of papers citing papers by Jun Huan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Huan

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Huan. A scholar is included among the top collaborators of Jun Huan 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 Jun Huan. Jun Huan 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

Bock, Christian, et al.. (2023). Training Large-scale Foundation Models on Emerging AI Chips. 5821–5822. 4 indexed citations

Xiong, Haoyi, Jian Zhao, Zeyu Chen, et al.. (2022). GrOD : Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training. ACM Transactions on Knowledge Discovery from Data. 16(6). 1–25. 16 indexed citations

Huang, Siyu, Tianyang Wang, Haoyi Xiong, et al.. (2022). Temporal Output Discrepancy for Loss Estimation-Based Active Learning. IEEE Transactions on Neural Networks and Learning Systems. 35(2). 2109–2123. 6 indexed citations

Li, Xingjian, Haoyi Xiong, Zeyu Chen, et al.. (2021). “In-Network Ensemble”: Deep Ensemble Learning with Diversified Knowledge Distillation. ACM Transactions on Intelligent Systems and Technology. 12(5). 1–19. 8 indexed citations

Yan, Shuicheng, Jiahui Yu, Nebojša Jojić, Jun Huan, & Thomas S. Huang. (2020). FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary. arXiv (Cornell University). 5 indexed citations

Wu, Jingfeng, Wenqing Hu, Haoyi Xiong, et al.. (2020). On the noisy gradient descent that generalizes as SGD. ePrints Soton (University of Southampton). 1 indexed citations

Huang, Siyu, Haoyi Xiong, Zhi-Qi Cheng, et al.. (2020). Generating Person Images with Appearance-aware Pose Stylizer. 623–629. 20 indexed citations

Wu, Jingfeng, Wenqing Hu, Haoyi Xiong, Jun Huan, & Zhanxing Zhu. (2019). The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation.. arXiv (Cornell University). 1 indexed citations

Li, Xingjian, et al.. (2019). Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks.. International Conference on Learning Representations. 2 indexed citations

10.

Xiong, Haoyi, et al.. (2019). Towards Making Deep Transfer Learning Never Hurt. 578–587. 11 indexed citations

11.

Zhou, Qiang, Wenan Zhou, Bin Yang, & Jun Huan. (2019). Deep cycle autoencoder for unsupervised domain adaptation with generative adversarial networks. IET Computer Vision. 13(7). 659–665. 4 indexed citations

12.

Xiong, Haoyi, Jiang Bian, Zhanxing Zhu, et al.. (2019). SpHMC: Spectral Hamiltonian Monte Carlo. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 5516–5524. 2 indexed citations

13.

Chen, Xiaoyang, Hongwei Huo, Jun Huan, & Jeffrey Scott Vitter. (2018). An efficient algorithm for graph edit distance computation. Knowledge-Based Systems. 163. 762–775. 21 indexed citations

14.

Lan, Chao, et al.. (2016). Partial Collective Matrix Factorization and its PAC Bound.. 1 indexed citations

15.

Zhang, Jintao & Jun Huan. (2012). Inductive multi-task learning with multiple view data. 543–551. 90 indexed citations

16.

Bandyopadhyay, Deepak, Jun Huan, Jinze Liu, et al.. (2010). Functional Neighbors: Inferring Relationships between Nonhomologous Protein Families Using Family-Specific Packing Motifs. IEEE Transactions on Information Technology in Biomedicine. 14(5). 1137–1143. 6 indexed citations

17.

Huan, Jun, et al.. (2010). Analysis of network topological features for identifying potential drug targets. 2 indexed citations

18.

Huan, Jun, et al.. (2009). GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 7(2). 197–207. 7 indexed citations

19.

Huan, Jun, et al.. (2004). Mining spatial motifs from protein structure graphs. 6 indexed citations

20.

Huan, Jun, Wei Wang, Deepak Bandyopadhyay, et al.. (2004). Mining protein family specific residue packing patterns from protein structure graphs. 308–315. 68 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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