Warren He
Impact in
- Artificial Intelligence top 2%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Signal Processing top 5%
- Advanced Malware Detection Techniques
Papers in
- Software 1
- Software Testing and Debugging Techniques 1
-
- Adversarial Robustness in Machine Learning 7
- Anomaly Detection Techniques and Applications 2
- Explainable Artificial Intelligence (XAI) 1
- Co-authors
- Dawn SongBo LiChaowei XiaoMingyan LiuJun-Yan ZhuXinyun ChenNicholas CarliniJun Zhu
- Journals
- International Conference on Learning Representations (3 papers)eScholarship (California Digital Library) (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Warren He
6 papers receiving 592 citations
Hit Papers
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 545
- Signal Processing 174
- Computer Vision and Pattern Recognition 198
- Hardware and Architecture 38
- Health Informatics 5
Countries citing papers authored by Warren He
This map shows the geographic impact of Warren He'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 Warren He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Warren He more than expected).
Fields of papers citing papers by Warren He
This network shows the impact of papers produced by Warren He. 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 Warren He. The network helps show where Warren He may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Warren He, 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 | 2022 | 0 | |
| 2 | Spatially transformed adversarial examples | 2018 | 22 |
| 3 | Black-box attacks on deep neural networks via gradient estimation | 2018 | 2 |
| 4 | Decision Boundary Analysis of Adversarial Examples | 2018 | 33 |
| 5 | Generating Adversarial Examples with Adversarial Networks Hit paper breakdown → | 2018 | 463 |
| 6 | Adaptive and Diverse Techniques for Generating Adversarial Examples | 2018 | 1 |
| 7 | Adversarial Example Defense: Ensembles of Weak Defenses are not Strong | 2017 | 94 |
About Warren He
Warren He is a scholar working on Software, Artificial Intelligence, Signal Processing, Infectious Diseases and Organic Chemistry, having authored 7 papers that have together received 615 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (7 papers), Anomaly Detection Techniques and Applications (2 papers), Advanced Malware Detection Techniques (2 papers), Explainable Artificial Intelligence (XAI) (1 paper) and Software Testing and Debugging Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (545 citations), Signal Processing (174 citations), Computer Vision and Pattern Recognition (198 citations), Hardware and Architecture (38 citations) and Health Informatics (5 citations). Warren He has collaborated with scholars based in United States and China. Frequent co-authors include Dawn Song, Bo Li, Chaowei Xiao, Mingyan Liu, Jun-Yan Zhu, Xinyun Chen, Nicholas Carlini, Jun Zhu, Bo Li and Bo Li. Their work appears in journals such as International Conference on Learning Representations and eScholarship (California Digital Library).
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.