Claudio Michaelis
Impact in
- Health Informatics top 2%
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Explainable Artificial Intelligence (XAI)
- Anomaly Detection Techniques and Applications
- Topic Modeling
- Natural Language Processing Techniques
Papers in
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- Face Recognition and Perception 2
- Motor Control and Adaptation 1
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- Action Observation and Synchronization 1
- Co-authors
- Matthias Bethge (4 shared papers)Felix A. Wichmann (3 shared papers)Robert Geirhos (3 shared papers)Wieland Brendel (3 shared papers)Jörn-Henrik Jacobsen (1 shared paper)Richard S. Zemel (1 shared paper)Pranav Mamidanna (1 shared paper)Alexander Mathis (1 shared paper)
- Journals
- eLife (1 paper)Nature Machine Intelligence (1 paper)Journal of Vision (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- GermanyUnited KingdomDenmark
In The Last Decade
Claudio Michaelis
4 papers receiving 1.1k citations
Claudio Michaelis's Hit Papers
Peers
Comparison fields: 5 of 127
- Health Informatics 59
- Artificial Intelligence 609
- Computer Vision and Pattern Recognition 385
- Cognitive Neuroscience 123
- Biophysics 37
Countries citing papers authored by Claudio Michaelis
This map shows the geographic impact of Claudio Michaelis'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 Claudio Michaelis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Claudio Michaelis more than expected).
Fields of papers citing papers by Claudio Michaelis
This network shows the impact of papers produced by Claudio Michaelis. 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 Claudio Michaelis. The network helps show where Claudio Michaelis may publish in the future.
Co-authors
The 10 scholars most cited alongside Claudio Michaelis, 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 | Shortcut learning in deep neural networks Hit paper breakdown → | 2020 | 870 |
| 2 | 2018 | 266 | |
| 3 | 2023 | 8 | |
| 4 | 2019 | 3 |
About Claudio Michaelis
Claudio Michaelis is a scholar working on Cognitive Neuroscience, Social Psychology, Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering, having authored 4 papers that have together received 1.1k indexed citations. Recurring topics across this work include Face Recognition and Perception (2 papers), Visual Attention and Saliency Detection (1 paper), Muscle activation and electromyography studies (1 paper), Infrared Target Detection Methodologies (1 paper), Adversarial Robustness in Machine Learning (1 paper), Action Observation and Synchronization (1 paper), Motor Control and Adaptation (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Health Informatics (59 citations), Artificial Intelligence (609 citations), Computer Vision and Pattern Recognition (385 citations), Cognitive Neuroscience (123 citations) and Biophysics (37 citations). Claudio Michaelis has collaborated with scholars based in Germany, United Kingdom and Denmark. Frequent co-authors include Matthias Bethge, Felix A. Wichmann, Robert Geirhos, Wieland Brendel, Jörn-Henrik Jacobsen, Richard S. Zemel, Pranav Mamidanna, Alexander Mathis, Mackenzie Weygandt Mathis and Jonas Rauber. Their work appears in journals such as eLife, Nature Machine Intelligence, Journal of Vision 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.