Sandra Ebert
Impact in
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- Context-Aware Activity Recognition Systems
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
Papers in
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- Multimodal Machine Learning Applications 1
- Context-Aware Activity Recognition Systems 1
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- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 1
- Machine Learning and Algorithms 1
- Co-authors
- Bernt Schiele (3 shared papers)Marcus Rohrbach (1 shared paper)Diane Larlus (1 shared paper)Maja Stikic (1 shared paper)Mario Fritz (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Max Planck Digital Library (1 paper)
In The Last Decade
Sandra Ebert
3 papers receiving 275 citations
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 195
- Artificial Intelligence 164
- Computer Science Applications 17
- Human-Computer Interaction 14
- Transportation 13
Countries citing papers authored by Sandra Ebert
This map shows the geographic impact of Sandra Ebert'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 Sandra Ebert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sandra Ebert more than expected).
Fields of papers citing papers by Sandra Ebert
This network shows the impact of papers produced by Sandra Ebert. 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 Sandra Ebert. The network helps show where Sandra Ebert may publish in the future.
Co-authors
The 5 scholars most cited alongside Sandra Ebert, 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 | 2011 | 119 | |
| 2 | Transfer Learning in a Transductive Setting | 2013 | 110 |
| 3 | 2012 | 55 |
About Sandra Ebert
Sandra Ebert is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Pediatrics, Perinatology and Child Health and Transportation, having authored 3 papers that have together received 284 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), IoT and Edge/Fog Computing (1 paper), Machine Learning and Data Classification (1 paper), Machine Learning and Algorithms (1 paper), Multimodal Machine Learning Applications (1 paper), Neonatal and fetal brain pathology (1 paper), Context-Aware Activity Recognition Systems (1 paper) and Human Mobility and Location-Based Analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (195 citations), Artificial Intelligence (164 citations), Computer Science Applications (17 citations), Human-Computer Interaction (14 citations) and Transportation (13 citations). Sandra Ebert has collaborated with scholars based in Germany and France. Frequent co-authors include Bernt Schiele, Marcus Rohrbach, Diane Larlus, Maja Stikic and Mario Fritz. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence and Max Planck 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.