Yuxiang Wu

2.6k total citations · 1 hit paper
17 papers, 1.4k citations indexed

About

Yuxiang Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Yuxiang Wu has authored 17 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 2 papers in Control and Systems Engineering. Recurrent topics in Yuxiang Wu's work include Topic Modeling (7 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (4 papers). Yuxiang Wu is often cited by papers focused on Topic Modeling (7 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (4 papers). Yuxiang Wu collaborates with scholars based in China, Canada and United Kingdom. Yuxiang Wu's co-authors include Sebastian Riedel, Tim Rocktäschel, Alexander Miller, Patrick Lewis, Fabio Petroni, Anton Bakhtin, Baotian Hu, Qiang Yang, Ying Wei and Yu Zhang and has published in prestigious journals such as PLoS Computational Biology, Signal Processing and Engineering Applications of Artificial Intelligence.

In The Last Decade

Yuxiang Wu

17 papers receiving 1.3k 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.

Language Models as Knowledge Bases?

2019 1.0k citations Fabio Petroni, Tim Rocktäschel et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuxiang Wu China	10	1.2k	330	129	62	47	17	1.4k
Shuming Ma China	21	819 0.7×	272 0.8×	95 0.7×	32 0.5×	32 0.7×	53	1.0k
Ting Liu China	21	1.2k 1.0×	180 0.5×	179 1.4×	50 0.8×	85 1.8×	74	1.4k
Yequan Wang China	9	1.5k 1.3×	114 0.3×	202 1.6×	66 1.1×	26 0.6×	26	1.8k
I Ketut Gede Darma Putra Indonesia	13	562 0.5×	188 0.6×	459 3.6×	15 0.2×	38 0.8×	147	1.0k
Erna Budhiarti Nababan Indonesia	14	293 0.3×	101 0.3×	244 1.9×	34 0.5×	24 0.5×	125	670
Chengwei Qin China	10	371 0.3×	65 0.2×	61 0.5×	35 0.6×	36 0.8×	34	604
Jungyun Seo South Korea	15	922 0.8×	86 0.3×	323 2.5×	47 0.8×	45 1.0×	118	1.1k
Anna Saranti Austria	12	413 0.4×	94 0.3×	59 0.5×	30 0.5×	63 1.3×	27	790
Michael Roth Germany	16	725 0.6×	183 0.6×	125 1.0×	24 0.4×	38 0.8×	90	1.0k
Kostiantyn Kucher Sweden	14	355 0.3×	328 1.0×	70 0.5×	15 0.2×	23 0.5×	48	678

Countries citing papers authored by Yuxiang Wu

Since Specialization

Citations

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

Fields of papers citing papers by Yuxiang Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuxiang Wu

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

17 of 17 papers shown

1.

Wang, Zechen, Liwen Wang, Yuxiang Wu, et al.. (2023). A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Named Entity Recognition. 10 indexed citations

2.

Wu, Yuxiang, et al.. (2022). Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans. PLoS Computational Biology. 18(10). e1010594–e1010594. 5 indexed citations

3.

Wu, Yuxiang, Pasquale Minervini, Pontus Stenetorp, & Sebastian Riedel. (2021). Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints. 447–453. 3 indexed citations

4.

Zhang, Dennis, et al.. (2020). NetEase Cloud Music Data. Manufacturing & Service Operations Management. 24(1). 275–284. 10 indexed citations

5.

Petroni, Fabio, Patrick Lewis, Aleksandra Piktus, et al.. (2020). How Context Affects Language Models' Factual Predictions. UCL Discovery (University College London). 16 indexed citations

6.

Zhang, Dennis, et al.. (2020). NetEase Cloud Music Data. SSRN Electronic Journal. 3 indexed citations

7.

Wu, Yuxiang, Sebastian Riedel, Pasquale Minervini, & Pontus Stenetorp. (2020). Don’t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering. 3029–3039. 2 indexed citations

8.

Petroni, Fabio, Tim Rocktäschel, Sebastian Riedel, et al.. (2019). Language Models as Knowledge Bases?. 2463–2473. 1022 indexed citations breakdown →

9.

Xu, Kele, Yuxiang Wu, & Zhifeng Gao. (2019). Ultrasound-Based Silent Speech Interface using Sequential Convolutional Auto-encoder. 2194–2195. 8 indexed citations

10.

Wu, Yuxiang & Baotian Hu. (2018). Learning to Extract Coherent Summary via Deep Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 83 indexed citations

11.

Zhang, Yu, et al.. (2017). End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2237–2243. 128 indexed citations

12.

Wang, Qicong, Jinhao Zhao, Yehu Shen, et al.. (2016). Visual attributes based sparse multitask action recognition. 1767–1772. 1 indexed citations

13.

Wang, Qicong, Yuxiang Wu, Yehu Shen, Yong Liu, & Yunqi Lei. (2014). Supervised sparse manifold regression for head pose estimation in 3D space. Signal Processing. 112. 34–42. 10 indexed citations

14.

Zhou, Qing, Yuxiang Wu, Christine W. Chan, & Paitoon Tontiwachwuthikul. (2011). Modeling of the carbon dioxide capture process system using machine intelligence approaches. Engineering Applications of Artificial Intelligence. 24(4). 673–685. 27 indexed citations

15.

Zhou, Qing, Yuxiang Wu, Christine W. Chan, & Paitoon Tontiwachwuthikul. (2010). Applications of three data analysis techniques for modeling the carbon dioxide capture process. 1–4. 10 indexed citations

16.

Xia, Xinghui, Zhifeng Yang, & Yuxiang Wu. (2008). Incorporating Eco-environmental Water Requirements in Integrated Evaluation of Water Quality and Quantity—A Study for the Yellow River. Water Resources Management. 23(6). 1067–1079. 26 indexed citations

17.

Harrison, Robert, et al.. (2007). A Decision Support System for Filtering and Analysis of Carbon Dioxide Capture Data. 1380–1383. 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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