M. Yin
Impact in
-
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Advanced Graph Neural Networks
- Adversarial Robustness in Machine Learning
-
- Intellectual Property and Patents
Papers in
-
- Topic Modeling 3
- Domain Adaptation and Few-Shot Learning 2
- Anomaly Detection Techniques and Applications 1
- Adversarial Robustness in Machine Learning 1
- Advanced Text Analysis Techniques 1
-
- Fault Detection and Control Systems 1
- Co-authors
- Charles X. Ling (4 shared papers)Boyu Wang (1 shared paper)Yue Dong (1 shared paper)S. H. Oh (1 shared paper)Boyu Wang (2 shared papers)C. Wang (1 shared paper)
- Journals
- Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (1 paper)Expert Systems with Applications (1 paper)Transactions of the Association for Computational Linguistics (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- CanadaUnited States
In The Last Decade
M. Yin
2 papers receiving 6 citations
Peers
Comparison fields: 5 of 8
- Artificial Intelligence 5
- Management of Technology and Innovation 1
- Statistical and Nonlinear Physics 1
- Computer Vision and Pattern Recognition 1
- Information Systems 1
Countries citing papers authored by M. Yin
This map shows the geographic impact of M. Yin'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 M. Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Yin more than expected).
Fields of papers citing papers by M. Yin
This network shows the impact of papers produced by M. Yin. 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 M. Yin. The network helps show where M. Yin may publish in the future.
Co-authors
The 7 scholars most cited alongside M. Yin, 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 | 2023 | 5 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 1993 | 0 |
About M. Yin
M. Yin is a scholar working on Artificial Intelligence, Control and Systems Engineering, Information Systems, Computer Vision and Pattern Recognition and Radiation, having authored 5 papers that have together received 6 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Anomaly Detection Techniques and Applications (1 paper), Radioactive Decay and Measurement Techniques (1 paper), Adversarial Robustness in Machine Learning (1 paper), Fault Detection and Control Systems (1 paper), Scientific Measurement and Uncertainty Evaluation (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (5 citations), Management of Technology and Innovation (1 citation), Statistical and Nonlinear Physics (1 citation), Computer Vision and Pattern Recognition (1 citation) and Information Systems (1 citation). M. Yin has collaborated with scholars based in Canada and United States. Frequent co-authors include Charles X. Ling, Boyu Wang, Boyu Wang, Yue Dong, S. H. Oh, Boyu Wang and C. Wang. Their work appears in journals such as Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Expert Systems with Applications, Transactions of the Association for Computational Linguistics and Proceedings of the AAAI Conference on Artificial Intelligence.
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.