Chas Leichner
Impact in
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
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- Industrial Vision Systems and Defect Detection
Papers in
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- Advanced Neural Network Applications 3
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- Adversarial Robustness in Machine Learning 3
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Fan Yang (1 shared paper)Weijun Wang (1 shared paper)Berkin Akin (1 shared paper)Vaibhav Aggarwal (1 shared paper)Daniele Moro (1 shared paper)Marco Fornoni (1 shared paper)Andrew W. Howard (1 shared paper)Chengxi Ye (1 shared paper)
- Journals
- Nature Communications (1 paper)Lecture notes in computer science (2 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesHong KongUnited Arab Emirates
In The Last Decade
Chas Leichner
5 papers receiving 320 citations
Chas Leichner's Hit Papers
Peers
Comparison fields: 5 of 67
- Computer Vision and Pattern Recognition 106
- Industrial and Manufacturing Engineering 34
- Media Technology 22
- Aerospace Engineering 33
- Human-Computer Interaction 7
Countries citing papers authored by Chas Leichner
This map shows the geographic impact of Chas Leichner'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 Chas Leichner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chas Leichner more than expected).
Fields of papers citing papers by Chas Leichner
This network shows the impact of papers produced by Chas Leichner. 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 Chas Leichner. The network helps show where Chas Leichner may publish in the future.
Co-authors
The 25 scholars most cited alongside Chas Leichner, 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 | MobileNetV4: Universal Models for the Mobile Ecosystem Hit paper breakdown → | 2024 | 290 |
| 2 | 2019 | 33 | |
| 3 | 2022 | 4 | |
| 4 | 2024 | 3 | |
| 5 | 2021 | 2 |
About Chas Leichner
Chas Leichner is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications and Molecular Biology, having authored 5 papers that have together received 332 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Advanced Neural Network Applications (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), CRISPR and Genetic Engineering (1 paper), Opportunistic and Delay-Tolerant Networks (1 paper), IoT and Edge/Fog Computing (1 paper), RNA and protein synthesis mechanisms (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (106 citations), Industrial and Manufacturing Engineering (34 citations), Media Technology (22 citations), Aerospace Engineering (33 citations) and Human-Computer Interaction (7 citations). Chas Leichner has collaborated with scholars based in United States, Hong Kong and United Arab Emirates. Frequent co-authors include Fan Yang, Weijun Wang, Berkin Akin, Vaibhav Aggarwal, Daniele Moro, Marco Fornoni, Andrew W. Howard, Chengxi Ye, Vlado Dančík and Luke W. Koblan. Their work appears in journals such as Nature Communications, Lecture notes in computer science 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.