Dongxian Wu

739 total citations
6 papers, 73 citations indexed

About

Dongxian Wu is a scholar working on Artificial Intelligence, Computer Networks and Communications and Molecular Biology. According to data from OpenAlex, Dongxian Wu has authored 6 papers receiving a total of 73 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 1 paper in Computer Networks and Communications and 1 paper in Molecular Biology. Recurrent topics in Dongxian Wu's work include Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (3 papers) and Machine Learning and Data Classification (2 papers). Dongxian Wu is often cited by papers focused on Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (3 papers) and Machine Learning and Data Classification (2 papers). Dongxian Wu collaborates with scholars based in China and Japan. Dongxian Wu's co-authors include Shu‐Tao Xia, Yisen Wang, Yiming Li, Yong Jiang, Ying-Hua Gao, Linghui Zhu, Tao Dai, Yan Feng, Bin Chen and Jian Lü and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and Neural Information Processing Systems.

In The Last Decade

Dongxian Wu

5 papers receiving 73 citations

Peers

Countries citing papers authored by Dongxian Wu

Since Specialization

Citations

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

Fields of papers citing papers by Dongxian Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongxian Wu

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

All Works

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6 of 6 papers shown

#	Work	Indexed citations
1	On the Effectiveness of Adversarial Training Against Backdoor Attacks 2023 ·IEEE Transactions on Neural Networks and Learning Systems ·(unknown), Dongxian Wu, Jingfeng Zhang, (unknown), Shu‐Tao Xia, (unknown), Masashi Sugiyama	9
2	Not All Samples Are Born Equal: Towards Effective Clean-Label Backdoor Attacks 2023 ·Pattern Recognition ·Ying-Hua Gao, Yiming Li, Linghui Zhu, Dongxian Wu, Yong Jiang, Shu‐Tao Xia	21
3	Towards Robust Model Watermark via Reducing Parametric Vulnerability 2023 ·(unknown), Yiming Li, Dongxian Wu, Shu‐Tao Xia	6
4	Adversarial Weight Perturbation Helps Robust Generalization 2020 ·Neural Information Processing Systems ·Dongxian Wu, Shu‐Tao Xia, Yisen Wang	24
5	Temporal Calibrated Regularization for Robust Noisy Label Learning 2020 ·Dongxian Wu, Yisen Wang, Zhuobin Zheng, Shu‐Tao Xia	0
6	DIPDefend: Deep Image Prior Driven Defense against Adversarial Examples 2020 ·Tao Dai, Yan Feng, Dongxian Wu, Bin Chen, Jian Lü, Yong Jiang, Shu‐Tao Xia	13

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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