Long-Kai Huang

749 total citations · 1 hit paper
18 papers, 447 citations indexed

About

Long-Kai Huang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Long-Kai Huang has authored 18 papers receiving a total of 447 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 4 papers in Molecular Biology. Recurrent topics in Long-Kai Huang's work include Advanced Image and Video Retrieval Techniques (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Video Surveillance and Tracking Methods (4 papers). Long-Kai Huang is often cited by papers focused on Advanced Image and Video Retrieval Techniques (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Video Surveillance and Tracking Methods (4 papers). Long-Kai Huang collaborates with scholars based in China, Hong Kong and United States. Long-Kai Huang's co-authors include Alessandro Bozzon, Zhu Sun, Jie Yang, Chi Xu, Jie Zhang, Wei‐Shi Zheng, Qiang Yang, Sinno Jialin Pan, Yu Rong and Yatao Bian and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Long-Kai Huang

17 papers receiving 442 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.

Recurrent knowledge graph embedding for effective recommendation

2018 245 citations Zhu Sun, Jie Yang et al. Research Repository (Delft University of Technology) profile →

Peers

Long-Kai Huang

AI

IS

CVPR

CNC

MB

Kelong Mao China

Houye Ji China

Deyu Bo China

Fangli Xu China

Aravind Sankar United States

Qiaoyu Tan United States

Nian Liu China

Hongliang Fei United States

Yuanfu Lu China

X. D. Zhang United States

Kelong Mao China

Long-Kai Huang

337 ×1.2

AI

292 ×1.5

IS

102 ×0.6

CVPR

51 ×1.0

CNC

20 ×0.8

MB

Citations per year, relative to Long-Kai Huang Long-Kai Huang (= 1×) peers Kelong Mao

Countries citing papers authored by Long-Kai Huang

Since Specialization

Citations

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

Fields of papers citing papers by Long-Kai Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Long-Kai Huang. 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 Long-Kai Huang. The network helps show where Long-Kai Huang may publish in the future.

Co-authorship network of co-authors of Long-Kai Huang

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

18 of 18 papers shown

1.

Huang, Long-Kai, et al.. (2025). Annotation-guided Protein Design with Multi-Level Domain Alignment. 1855–1866. 1 indexed citations

2.

Liu, Linjing, Fang Wang, Long-Kai Huang, et al.. (2025). A pre-trained large generative model for translating single-cell transcriptomes to proteomes. Nature Biomedical Engineering.

3.

Yang, Fan, Fang Wang, Long-Kai Huang, et al.. (2024). Reply to: Deeper evaluation of a single-cell foundation model. Nature Machine Intelligence. 6(12). 1447–1450. 1 indexed citations

4.

Huang, Xiao, et al.. (2023). Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation. IEEE Transactions on Knowledge and Data Engineering. 35(12). 12721–12735. 18 indexed citations

5.

Ji, Yuanfeng, Lu Zhang, Jiaxiang Wu, et al.. (2023). DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8023–8031. 19 indexed citations

6.

Wang, Fang, Fan Yang, Long-Kai Huang, et al.. (2023). Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling. Nature Machine Intelligence. 5(11). 1236–1249. 14 indexed citations

7.

Huang, Long-Kai, et al.. (2023). Concept-wise Fine-tuning Matters in Preventing Negative Transfer. 18707–18717. 1 indexed citations

8.

Zhang, Jiying, Xi Xiao, Long-Kai Huang, Yu Rong, & Yatao Bian. (2022). Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 3730–3736. 17 indexed citations

9.

Yao, Huaxiu, et al.. (2021). Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery. Neural Information Processing Systems. 34. 1 indexed citations

10.

Yao, Huaxiu, Long-Kai Huang, Linjun Zhang, et al.. (2021). Improving Generalization in Meta-learning via Task Augmentation. 11887–11897. 10 indexed citations

11.

Huang, Long-Kai & Sinno Jialin Pan. (2020). Communication-Efficient Distributed PCA by Riemannian Optimization. International Conference on Machine Learning. 1. 4465–4474. 5 indexed citations

12.

Weng, Zhenyu, et al.. (2019). A fast online spherical hashing method based on data sampling for large scale image retrieval. Neurocomputing. 364. 209–218. 5 indexed citations

13.

Huang, Long-Kai, et al.. (2019). Accelerate Learning of Deep Hashing With Gradient Attention. 5270–5279. 20 indexed citations

14.

Sun, Zhu, Jie Yang, Jie Zhang, et al.. (2018). Recurrent knowledge graph embedding for effective recommendation. Research Repository (Delft University of Technology). 297–305. 245 indexed citations breakdown →

15.

Huang, Long-Kai, Qiang Yang, & Wei‐Shi Zheng. (2017). Online Hashing. IEEE Transactions on Neural Networks and Learning Systems. 29(6). 2309–2322. 40 indexed citations

16.

Huang, Long-Kai & Sinno Jialin Pan. (2016). Class-wise supervised hashing with label embedding and active bits. International Joint Conference on Artificial Intelligence. 1585–1591. 8 indexed citations

17.

Huang, Long-Kai, Qiang Yang, & Wei‐Shi Zheng. (2013). Online hashing. International Joint Conference on Artificial Intelligence. 1422–1428. 36 indexed citations

18.

Yang, Qiang, Long-Kai Huang, Wei‐Shi Zheng, & Yingbiao Ling. (2013). Smart hashing update for fast response. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1855–1861. 6 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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