Yeming Wen
Impact in
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- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
- Gaussian Processes and Bayesian Inference
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
Papers in
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- Stochastic Gradient Optimization Techniques 2
- Natural Language Processing Techniques 1
- Adversarial Robustness in Machine Learning 1
- Topic Modeling 1
- Computational Physics and Python Applications 1
- Domain Adaptation and Few-Shot Learning 1
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- Advanced Neural Network Applications 3
- Co-authors
- Jimmy Ba (4 shared papers)Dustin Tran (2 shared papers)Henryk Michalewski (1 shared paper)Charles Sutton (1 shared paper)Michele Catasta (1 shared paper)Harris Chan (2 shared papers)Pengcheng Yin (1 shared paper)Guodong Zhang (2 shared papers)
- Journals
- International Conference on Artificial Intelligence and Statistics (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United StatesCanada
In The Last Decade
Yeming Wen
5 papers receiving 55 citations
Peers
Comparison fields: 5 of 30
- Artificial Intelligence 39
- Computer Vision and Pattern Recognition 22
- Health Informatics 1
- Family Practice 1
- Signal Processing 4
Countries citing papers authored by Yeming Wen
This map shows the geographic impact of Yeming Wen'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 Yeming Wen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yeming Wen more than expected).
Fields of papers citing papers by Yeming Wen
This network shows the impact of papers produced by Yeming Wen. 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 Yeming Wen. The network helps show where Yeming Wen may publish in the future.
Co-authors
The 16 scholars most cited alongside Yeming Wen, 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 | 2020 | 34 | |
| 2 | 2023 | 9 | |
| 3 | Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise. | 2019 | 7 |
| 4 | Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches | 2018 | 6 |
| 5 | An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise | 2020 | 1 |
About Yeming Wen
Yeming Wen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 57 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Sparse and Compressive Sensing Techniques (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Natural Language Processing Techniques (1 paper), Adversarial Robustness in Machine Learning (1 paper), Topic Modeling (1 paper), Computational Physics and Python Applications (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Artificial Intelligence (39 citations), Computer Vision and Pattern Recognition (22 citations), Health Informatics (1 citation), Family Practice (1 citation) and Signal Processing (4 citations). Yeming Wen has collaborated with scholars based in United States and Canada. Frequent co-authors include Jimmy Ba, Dustin Tran, Henryk Michalewski, Charles Sutton, Michele Catasta, Harris Chan, Pengcheng Yin, Guodong Zhang, Kefan Xiao and Abhishek S. Rao. Their work appears in journals such as International Conference on Artificial Intelligence and Statistics and arXiv (Cornell University).
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.