Guyue Huang

540 total citations
8 papers, 305 citations indexed

About

Guyue Huang is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Guyue Huang has authored 8 papers receiving a total of 305 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Hardware and Architecture and 4 papers in Artificial Intelligence. Recurrent topics in Guyue Huang's work include Parallel Computing and Optimization Techniques (5 papers), Advanced Neural Network Applications (3 papers) and Stochastic Gradient Optimization Techniques (2 papers). Guyue Huang is often cited by papers focused on Parallel Computing and Optimization Techniques (5 papers), Advanced Neural Network Applications (3 papers) and Stochastic Gradient Optimization Techniques (2 papers). Guyue Huang collaborates with scholars based in China, United States and Hong Kong. Guyue Huang's co-authors include Guohao Dai, Huazhong Yang, Yu Wang, Hengrui Zhang, Xuefei Ning, Haoyu Yang, Bei Yu, Mingyuan Ma, Yifan He and Juejian Wu and has published in prestigious journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ACM Transactions on Design Automation of Electronic Systems and Rare & Special e-Zone (The Hong Kong University of Science and Technology).

In The Last Decade

Guyue Huang

7 papers receiving 298 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Guyue Huang China	7	145	120	117	79	35	8	305
Siying Feng United States	9	128 0.9×	90 0.8×	226 1.9×	122 1.5×	156 4.5×	19	378
Aporva Amarnath United States	9	148 1.0×	89 0.7×	241 2.1×	120 1.5×	181 5.2×	16	399
Linyan Mei Belgium	9	187 1.3×	66 0.6×	83 0.7×	122 1.5×	53 1.5×	17	300
Subhankar Pal United States	9	129 0.9×	95 0.8×	233 2.0×	126 1.6×	162 4.6×	24	379
Philipp Gysel United States	4	197 1.4×	123 1.0×	47 0.4×	251 3.2×	25 0.7×	5	347
Sheng-Chun Kao United States	8	125 0.9×	68 0.6×	118 1.0×	100 1.3×	76 2.2×	15	265
Steve Dai United States	15	319 2.2×	101 0.8×	486 4.2×	96 1.2×	240 6.9×	27	677
Victor A. Ying United States	6	303 2.1×	117 1.0×	256 2.2×	188 2.4×	160 4.6×	9	497
Joonho Song South Korea	7	179 1.2×	65 0.5×	106 0.9×	105 1.3×	88 2.5×	16	311
Dong-Hyeon Park United States	7	106 0.7×	86 0.7×	196 1.7×	115 1.5×	131 3.7×	8	312

Countries citing papers authored by Guyue Huang

Since Specialization

Citations

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

Fields of papers citing papers by Guyue Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guyue Huang

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

8 of 8 papers shown

1.

Xu, Jiaming, Shan Huang, Guyue Huang, et al.. (2024). Enabling Efficient Sparse Multiplications on GPUs With Heuristic Adaptability. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 44(6). 2226–2239.

2.

Huang, Guyue, et al.. (2023). RM-STC: Row-Merge Dataflow Inspired GPU Sparse Tensor Core for Energy-Efficient Sparse Acceleration. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 338–352. 8 indexed citations

3.

Wang, Xiaohui, Wei Yang, Ying Xiong, et al.. (2022). LightSeq2: Accelerated Training for Transformer-Based Models on GPUs. 1–14. 15 indexed citations

4.

Huang, Guyue, Haoran Li, Minghai Qin, et al.. (2022). Shfl-BW. Proceedings of the 59th ACM/IEEE Design Automation Conference. 1153–1158. 9 indexed citations

5.

Dai, Guohao, Guyue Huang, Shang Fa Yang, et al.. (2022). Heuristic adaptability to input dynamics for SpMM on GPUs. Proceedings of the 59th ACM/IEEE Design Automation Conference. 595–600. 17 indexed citations

6.

Huang, Guyue, Yifan He, Jialong Liu, et al.. (2021). Machine Learning for Electronic Design Automation: A Survey. ACM Transactions on Design Automation of Electronic Systems. 26(5). 1–46. 180 indexed citations

7.

Dai, Guohao, et al.. (2021). Exploiting Online Locality and Reduction Parallelism for Sampled Dense Matrix Multiplication on GPUs. 567–574. 6 indexed citations

8.

Huang, Guyue, Guohao Dai, Yu Wang, & Huazhong Yang. (2020). GE-SpMM: General-Purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks. 1–12. 70 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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