Niannan Xue

891 total citations
10 papers, 271 citations indexed

About

Niannan Xue is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Signal Processing. According to data from OpenAlex, Niannan Xue has authored 10 papers receiving a total of 271 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 3 papers in Information Systems and 3 papers in Signal Processing. Recurrent topics in Niannan Xue's work include Sparse and Compressive Sensing Techniques (3 papers), Face and Expression Recognition (3 papers) and Recommender Systems and Techniques (3 papers). Niannan Xue is often cited by papers focused on Sparse and Compressive Sensing Techniques (3 papers), Face and Expression Recognition (3 papers) and Recommender Systems and Techniques (3 papers). Niannan Xue collaborates with scholars based in United Kingdom, Finland and China. Niannan Xue's co-authors include Stefanos Zafeiriou, Jiankang Deng, Shiyang Cheng, Yuxiang Zhou, Mounia Lalmas, Rishabh Mehrotra, Yannis Panagakis, Zhenguo Li, Huifeng Guo and Ruiming Tang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering and Research Explorer (The University of Manchester).

In The Last Decade

Niannan Xue

10 papers receiving 262 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Niannan Xue United Kingdom	8	195	62	56	54	47	10	271
Ruohan Zhan United States	7	161 0.8×	18 0.3×	10 0.2×	52 1.0×	45 1.0×	10	242
Jiang Du China	8	108 0.6×	53 0.9×	9 0.2×	79 1.5×	38 0.8×	30	263
Yunshan Ma Singapore	12	172 0.9×	14 0.2×	30 0.5×	143 2.6×	223 4.7×	28	370
Paul Swoboda Germany	10	127 0.7×	15 0.2×	28 0.5×	23 0.4×	83 1.8×	22	248
Zhenxiao Luo China	9	138 0.7×	77 1.2×	6 0.1×	84 1.6×	65 1.4×	20	253
Hyokun Yun United States	5	59 0.3×	7 0.1×	14 0.3×	53 1.0×	92 2.0×	7	162
Wu-Jun Li China	5	66 0.3×	17 0.3×	9 0.2×	123 2.3×	135 2.9×	12	220
Sungjoo Ha South Korea	5	109 0.6×	108 1.7×	7 0.1×	20 0.4×	128 2.7×	6	257
Md Abul Bashar Australia	7	58 0.3×	12 0.2×	8 0.1×	29 0.5×	112 2.4×	23	203

Countries citing papers authored by Niannan Xue

Since Specialization

Citations

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

Fields of papers citing papers by Niannan Xue

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Niannan Xue

This figure shows the co-authorship network connecting the top 25 collaborators of Niannan Xue. A scholar is included among the top collaborators of Niannan Xue 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 Niannan Xue. Niannan Xue is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

Mehrotra, Rishabh, Niannan Xue, & Mounia Lalmas. (2020). Bandit based Optimization of Multiple Objectives on a Music Streaming Platform. 3224–3233. 33 indexed citations

Xue, Niannan, Bin Liu, Huifeng Guo, et al.. (2020). AutoHash: Learning Higher-order Feature Interactions for Deep CTR Prediction. IEEE Transactions on Knowledge and Data Engineering. 1–1. 13 indexed citations

刘, 斌, Niannan Xue, Huifeng Guo, et al.. (2020). AutoGroup. 199–208. 29 indexed citations

Xue, Niannan, Jiankang Deng, Shiyang Cheng, Yannis Panagakis, & Stefanos Zafeiriou. (2019). Side Information for Face Completion: A Robust PCA Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(10). 2349–2364. 19 indexed citations

Guo, Jia, Jiankang Deng, Niannan Xue, & Stefanos Zafeiriou. (2018). Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment. arXiv (Cornell University). 44. 11 indexed citations

Deng, Jiankang, Shiyang Cheng, Niannan Xue, Yuxiang Zhou, & Stefanos Zafeiriou. (2018). UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition. Spiral (Imperial College London). 7093–7102. 145 indexed citations

Xue, Niannan, Jiankang Deng, Yannis Panagakis, & Stefanos Zafeiriou. (2018). Informed Non-Convex Robust Principal Component Analysis With Features. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 4 indexed citations

Xue, Niannan, et al.. (2017). Robust low-rank tensor modelling using Tucker and CP decomposition. 1185–1189. 8 indexed citations

Xue, Niannan, Yannis Panagakis, & Stefanos Zafeiriou. (2017). Side Information in Robust Principal Component Analysis: Algorithms and Applications. Middlesex University Research Repository (Middlesex University Of London). 4327–4335. 8 indexed citations

10.

Xue, Niannan, Wei Pan, & Yike Guo. (2016). Parameter estimation for gene regulatory networks: A two-stage MCMC Bayesian approach. Research Explorer (The University of Manchester). 1476–1479. 1 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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