Shangqian Gao

734 total citations
24 papers, 387 citations indexed

About

Shangqian Gao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Shangqian Gao has authored 24 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 2 papers in Computer Networks and Communications. Recurrent topics in Shangqian Gao's work include Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Adversarial Robustness in Machine Learning (5 papers). Shangqian Gao is often cited by papers focused on Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Adversarial Robustness in Machine Learning (5 papers). Shangqian Gao collaborates with scholars based in United States, China and Australia. Shangqian Gao's co-authors include Heng Huang, Feihu Huang, Jian Pei, Cheng Deng, Weidong Cai, Wei Liu, Yanhua Yang, Xinbo Gao, Dapeng Tao and Yu Kong and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural Networks and IEEE Transactions on Multimedia.

In The Last Decade

Shangqian Gao

23 papers receiving 382 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shangqian Gao United States	9	280	224	48	21	20	24	387
Chenglong Zhao China	7	297 1.1×	273 1.2×	15 0.3×	24 1.1×	9 0.5×	23	417
Constantinos Constantinopoulos Greece	7	123 0.4×	198 0.9×	17 0.4×	9 0.4×	25 1.3×	14	299
Chao Yao China	11	196 0.7×	179 0.8×	26 0.5×	18 0.9×	13 0.7×	31	397
Zinan Zeng Singapore	5	350 1.3×	268 1.2×	63 1.3×	29 1.4×	29 1.4×	6	461
Min Dong China	9	132 0.5×	87 0.4×	32 0.7×	14 0.7×	15 0.8×	40	305
Pradeep Natarajan United States	14	505 1.8×	238 1.1×	58 1.2×	13 0.6×	65 3.3×	38	656
Mingqing Hu China	9	588 2.1×	198 0.9×	20 0.4×	17 0.8×	5 0.3×	16	718
Yuyang Liu China	9	84 0.3×	122 0.5×	19 0.4×	17 0.8×	7 0.3×	14	215
Vassileios Balntas United Kingdom	7	436 1.6×	97 0.4×	17 0.4×	13 0.6×	6 0.3×	10	514

Countries citing papers authored by Shangqian Gao

Since Specialization

Citations

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

Fields of papers citing papers by Shangqian Gao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shangqian Gao

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

Gao, Shangqian, Ting Hua, Yen-Chang Hsu, Yilin Shen, & Hongxia Jin. (2024). Adaptive Rank Selections for Low-Rank Approximation of Language Models. 227–241. 1 indexed citations

Gao, Shangqian, Yanfu Zhang, Feihu Huang, & Heng Huang. (2024). BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks. 16090–16100. 4 indexed citations

Gao, Shangqian, et al.. (2024). Device-Wise Federated Network Pruning. 12342–12352. 1 indexed citations

Wang, Zhepeng, et al.. (2024). Unlocking Memorization in Large Language Models with Dynamic Soft Prompting. 9782–9796. 1 indexed citations

Gao, Shangqian, et al.. (2024). Auto- Train-Once: Controller Network Guided Automatic Network Pruning from Scratch. 16163–16173. 3 indexed citations

Huang, Feihu, Shangqian Gao, Jian Pei, & Heng Huang. (2024). Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity. IEEE Transactions on Pattern Analysis and Machine Intelligence. PP. 1–13. 1 indexed citations

Gao, Shangqian, et al.. (2024). Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment. 16058–16069. 2 indexed citations

Gao, Shangqian, et al.. (2024). Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12118–12126. 8 indexed citations

Gao, Shangqian, et al.. (2024). DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models. 72219–72244. 2 indexed citations

10.

Gao, Shangqian, et al.. (2023). EffConv: Efficient Learning of Kernel Sizes for Convolution Layers of CNNs. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7604–7612. 4 indexed citations

11.

Gao, Shangqian, Zeyu Zhang, Yanfu Zhang, Feihu Huang, & Heng Huang. (2023). Structural Alignment for Network Pruning through Partial Regularization. 17356–17366. 8 indexed citations

12.

Huang, Feihu & Shangqian Gao. (2023). Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(7). 1–11. 12 indexed citations

13.

Huang, Feihu & Shangqian Gao. (2022). Riemannian gradient methods for stochastic composition problems. Neural Networks. 153. 224–234. 1 indexed citations

14.

Zhang, Yanfu, Shangqian Gao, Jian Pei, & Heng Huang. (2022). Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2515–2523. 26 indexed citations

15.

Gao, Shangqian, Feihu Huang, Weidong Cai, & Heng Huang. (2021). Network Pruning via Performance Maximization. 9266–9276. 74 indexed citations

16.

Li, Chao, Shangqian Gao, Cheng Deng, Wei Liu, & Heng Huang. (2021). Adversarial Attack on Deep Cross-Modal Hamming Retrieval. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2198–2207. 2 indexed citations

17.

Huang, Feihu, Shangqian Gao, Jian Pei, & Heng Huang. (2020). Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization. 3 indexed citations

18.

Li, Chao, Shangqian Gao, Cheng Deng, De Xie, & Wei Liu. (2019). Cross-Modal Learning with Adversarial Samples. Neural Information Processing Systems. 32. 10791–10801. 16 indexed citations

19.

Gao, Shangqian, Cheng Deng, & Heng Huang. (2019). Cross Domain Model Compression by Structurally Weight Sharing. 8965–8974. 1 indexed citations

20.

Huang, Feihu, Shangqian Gao, Songcan Chen, & Heng Huang. (2019). Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization. 2549–2555. 12 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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