Sindy Löwe
Impact in
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- Industrial Vision Systems and Defect Detection
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
Papers in
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- Neural Networks and Applications 3
- Domain Adaptation and Few-Shot Learning 2
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- Music and Audio Processing 2
- Co-authors
- Paul Bergmann (1 shared paper)Michael Fauser (1 shared paper)David Sattlegger (1 shared paper)Carsten Steger (1 shared paper)Bastiaan S. Veeling (2 shared papers)Peter O’Connor (2 shared papers)Luisa Helena Bartocci Liboni (1 shared paper)Lyle Muller (1 shared paper)
- Journals
- Proceedings of the National Academy of Sciences (1 paper)UvA-DARE (University of Amsterdam) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- NetherlandsIndiaCanada
In The Last Decade
Sindy Löwe
4 papers receiving 202 citations
Peers
Comparison fields: 5 of 45
- Industrial and Manufacturing Engineering 65
- Artificial Intelligence 161
- Computer Vision and Pattern Recognition 53
- Media Technology 19
- Signal Processing 21
Countries citing papers authored by Sindy Löwe
This map shows the geographic impact of Sindy Löwe'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 Sindy Löwe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sindy Löwe more than expected).
Fields of papers citing papers by Sindy Löwe
This network shows the impact of papers produced by Sindy Löwe. 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 Sindy Löwe. The network helps show where Sindy Löwe may publish in the future.
Co-authors
The 11 scholars most cited alongside Sindy Löwe, 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 | 2019 | 187 | |
| 2 | Putting An End to End-to-End: Gradient-Isolated Learning of Representations | 2019 | 15 |
| 3 | Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning. | 2019 | 2 |
| 4 | 2025 | 2 |
About Sindy Löwe
Sindy Löwe is a scholar working on Artificial Intelligence, Signal Processing, Cognitive Neuroscience, Biophysics and Industrial and Manufacturing Engineering, having authored 4 papers that have together received 206 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Music and Audio Processing (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Industrial Vision Systems and Defect Detection (1 paper), Cell Image Analysis Techniques (1 paper), Manufacturing Process and Optimization (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (65 citations), Artificial Intelligence (161 citations), Computer Vision and Pattern Recognition (53 citations), Media Technology (19 citations) and Signal Processing (21 citations). Sindy Löwe has collaborated with scholars based in Netherlands, India and Canada. Frequent co-authors include Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger, Bastiaan S. Veeling, Peter O’Connor, Luisa Helena Bartocci Liboni, Lyle Muller, Roberto C. Budzinski and Max Welling. Their work appears in journals such as Proceedings of the National Academy of Sciences, UvA-DARE (University of Amsterdam) 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.