Xingjun Ma
- Artificial Intelligence top 0.5%
- Adversarial Robustness in Machine Learning 24
- Anomaly Detection Techniques and Applications 15
- Privacy-Preserving Technologies in Data 7
- Machine Learning and Data Classification 6
- Topic Modeling 4
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- Advanced Neural Network Applications 6
- Generative Adversarial Networks and Image Synthesis 4
- Digital Media Forensic Detection 4
- Health Informatics top 2%
- Signal Processing top 2%
- Software top 10%
Xingjun Ma
52 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Artificial Intelligence 1.9k
- Computer Vision and Pattern Recognition 892
- Health Informatics 54
- Signal Processing 283
- Software 41
Countries citing papers authored by Xingjun Ma
This map shows the geographic impact of Xingjun Ma'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 Xingjun Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xingjun Ma more than expected).
Fields of papers citing papers by Xingjun Ma
This network shows the impact of papers produced by Xingjun Ma. 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 Xingjun Ma. The network helps show where Xingjun Ma may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xingjun Ma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 27 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 1 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 7 | |
| 10 | Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | 2021 | 20 |
| 11 | Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks | 2021 | 3 |
| 12 | 2020 | 134 | |
| 13 | Improving Adversarial Robustness Requires Revisiting Misclassified Examples | 2020 | 153 |
| 14 | Understanding adversarial attacks on deep learning based medical image analysis systemsbreakdown → | 2020 | 304 |
| 15 | 2020 | 133 | |
| 16 | Towards Fair and Decentralized Privacy-Preserving Deep Learning | 2019 | 2 |
| 17 | Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain | 2019 | 15 |
| 18 | Dimensionality-Driven Learning with Noisy Labels | 2018 | 49 |
| 19 | Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality | 2018 | 176 |
| 20 | Production Situation and Technology Prospect of Medical Isotopes | 2016 | 1 |
About Xingjun Ma
Xingjun Ma is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Software, having authored 56 papers that have together received 2.6k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (24 papers), Anomaly Detection Techniques and Applications (15 papers), Privacy-Preserving Technologies in Data (7 papers), Machine Learning and Data Classification (6 papers), Advanced Neural Network Applications (6 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Topic Modeling (4 papers) and Digital Media Forensic Detection (4 papers). The work is most often cited by research in Artificial Intelligence (1.9k citations), Computer Vision and Pattern Recognition (892 citations) and Health Informatics (54 citations). Xingjun Ma has collaborated with scholars based in China, Australia and United States. Frequent co-authors include James Bailey, Yisen Wang, Jinfeng Yi, Yu–Gang Jiang, Yuan Luo, Jingjing Chen, Bojia Zi, Lin Gu, Yitian Zhao and Feng Lu. Their work appears in journals such as IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Software Engineering and Methodology, Knowledge and Information Systems, IEEE Transactions on Artificial Intelligence and Machine Learning.
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.