Jonas Rauber
Impact in
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Visual Attention and Saliency Detection 2
- Advanced Neural Network Applications 2
-
- Adversarial Robustness in Machine Learning 5
- Anomaly Detection Techniques and Applications 1
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Matthias Bethge (5 shared papers)Wieland Brendel (4 shared papers)R. Zimmermann (1 shared paper)Heiko H. Schütt (1 shared paper)Robert Geirhos (2 shared papers)Felix A. Wichmann (2 shared papers)Lukas Schott (1 shared paper)Matthias Kümmerer (1 shared paper)
- Journals
- International Journal of Computer Vision (1 paper)Fatigue & Fracture of Engineering Materials & Structures (1 paper)Journal of Vision (1 paper)Max Planck Digital Library (1 paper)Procedia Structural Integrity (1 paper)
- Partner nations
- GermanySwitzerlandChina
In The Last Decade
Jonas Rauber
9 papers receiving 350 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 240
- Health Informatics 8
- Computer Vision and Pattern Recognition 110
- Signal Processing 44
- Cognitive Neuroscience 45
Countries citing papers authored by Jonas Rauber
This map shows the geographic impact of Jonas Rauber'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 Jonas Rauber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Rauber more than expected).
Fields of papers citing papers by Jonas Rauber
This network shows the impact of papers produced by Jonas Rauber. 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 Jonas Rauber. The network helps show where Jonas Rauber may publish in the future.
Co-authors
The 14 scholars most cited alongside Jonas Rauber, 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 | Generalisation in humans and deep neural networks | 2018 | 130 |
| 2 | 2020 | 90 | |
| 3 | Towards the First Adversarially Robust Neural Network Model on MNIST | 2019 | 72 |
| 4 | Accurate, reliable and fast robustness evaluation | 2019 | 27 |
| 5 | 2019 | 25 | |
| 6 | 2019 | 8 | |
| 7 | 2020 | 7 | |
| 8 | 2019 | 3 | |
| 9 | 2019 | 1 |
About Jonas Rauber
Jonas Rauber is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Health Informatics, Metals and Alloys and Mechanics of Materials, having authored 9 papers that have together received 363 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (5 papers), Infrared Target Detection Methodologies (2 papers), Visual Attention and Saliency Detection (2 papers), Fatigue and fracture mechanics (2 papers), Advanced Neural Network Applications (2 papers), Metallurgy and Material Forming (1 paper), Anomaly Detection Techniques and Applications (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Artificial Intelligence (240 citations), Health Informatics (8 citations), Computer Vision and Pattern Recognition (110 citations), Signal Processing (44 citations) and Cognitive Neuroscience (45 citations). Jonas Rauber has collaborated with scholars based in Germany, Switzerland and China. Frequent co-authors include Matthias Bethge, Wieland Brendel, R. Zimmermann, Heiko H. Schütt, Robert Geirhos, Felix A. Wichmann, Lukas Schott, Matthias Kümmerer, Christian Motz and Florian Schaefer. Their work appears in journals such as International Journal of Computer Vision, Fatigue & Fracture of Engineering Materials & Structures, Journal of Vision, Max Planck Digital Library and Procedia Structural Integrity.
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.