Yisen Wang
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Molecular Biology
- Topics
- Anomaly Detection Techniques and Applications (13 papers)Adversarial Robustness in Machine Learning (11 papers)Advanced Malware Detection Techniques (10 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Yisen Wang
50 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Artificial Intelligence 1.4k
- Computer Vision and Pattern Recognition 661
- Signal Processing 202
- Radiology, Nuclear Medicine and Imaging 157
- Molecular Biology 152
Countries citing papers authored by Yisen Wang
This map shows the geographic impact of Yisen Wang'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 Yisen Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yisen Wang more than expected).
Fields of papers citing papers by Yisen Wang
This network shows the impact of papers produced by Yisen Wang. 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 Yisen Wang. The network helps show where Yisen Wang may publish in the future.
Co-authorship network of co-authors of Yisen Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yisen Wang. A scholar is included among the top collaborators of Yisen Wang 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 Yisen Wang. Yisen Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 7 | |
| 9 | 71 | |
| 10 | Efficient Equivariant Network | 7 |
| 11 | Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness | 4 |
| 12 | Clustering Effect of Adversarial Robust Models | 2 |
| 13 | Improving Adversarial Robustness Requires Revisiting Misclassified Examples | 153 |
| 14 | Adversarial Weight Perturbation Helps Robust Generalization | 24 |
| 15 | Understanding adversarial attacks on deep learning based medical image analysis systemsbreakdown → | 304 |
| 16 | 133 | |
| 17 | Decoupled Networks | 7 |
| 18 | Dimensionality-Driven Learning with Noisy Labels | 49 |
| 19 | Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality | 176 |
| 20 | Bernoulli random forests: closing the gap between theoretical consistency and empirical soundness | 5 |
About Yisen Wang
Yisen Wang is a scholar working on Software, Artificial Intelligence and Signal Processing, having authored 59 papers that have together received 1.9k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (13 papers), Adversarial Robustness in Machine Learning (11 papers) and Advanced Malware Detection Techniques (10 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Health Informatics (46 citations) and Computer Vision and Pattern Recognition (661 citations). Yisen Wang has collaborated with scholars based in China, United States and Australia. Frequent co-authors include James Bailey, Xingjun Ma, Jinfeng Yi, Yuan Luo, Shu‐Tao Xia, Feng Lu, Lin Gu, Yitian Zhao, Sarah Erfani and Quanquan Gu. Their work appears in journals such as PLoS ONE, IEEE Transactions on Image Processing and IEEE Access.
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.