Lukas Schott
Impact in
-
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
-
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
Papers in
-
- Adversarial Robustness in Machine Learning 3
- Anomaly Detection Techniques and Applications 2
-
- Bacillus and Francisella bacterial research 1
- Co-authors
- Matthias Bethge (3 shared papers)Wieland Brendel (3 shared papers)Jonas Rauber (2 shared papers)Shengdong Zhang (1 shared paper)Naveen Ramakrishnan (1 shared paper)Mohak Shah (1 shared paper)R. Zimmermann (1 shared paper)Evgenia Rusak (1 shared paper)
- Journals
- MPG.PuRe (Max Planck Society) (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- GermanyUnited States
In The Last Decade
Lukas Schott
4 papers receiving 89 citations
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 62
- Computer Vision and Pattern Recognition 27
- Software 5
- Signal Processing 10
- Biophysics 3
Countries citing papers authored by Lukas Schott
This map shows the geographic impact of Lukas Schott'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 Lukas Schott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lukas Schott more than expected).
Fields of papers citing papers by Lukas Schott
This network shows the impact of papers produced by Lukas Schott. 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 Lukas Schott. The network helps show where Lukas Schott may publish in the future.
Co-authors
The 9 scholars most cited alongside Lukas Schott, 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 | Towards the First Adversarially Robust Neural Network Model on MNIST | 2019 | 54 |
| 2 | Increasing the robustness of DNNs against image corruptions by playing the Game of Noise | 2020 | 16 |
| 3 | 2017 | 15 | |
| 4 | Robust Perception through Analysis by Synthesis. | 2018 | 6 |
About Lukas Schott
Lukas Schott is a scholar working on Artificial Intelligence, Molecular Biology, Signal Processing, Radiology, Nuclear Medicine and Imaging and Electrical and Electronic Engineering, having authored 4 papers that have together received 91 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers), Time Series Analysis and Forecasting (1 paper), COVID-19 diagnosis using AI (1 paper), Integrated Circuits and Semiconductor Failure Analysis (1 paper), Bacillus and Francisella bacterial research (1 paper) and Music and Audio Processing (1 paper). The work is most often cited by research in Artificial Intelligence (62 citations), Computer Vision and Pattern Recognition (27 citations), Software (5 citations), Signal Processing (10 citations) and Biophysics (3 citations). Lukas Schott has collaborated with scholars based in Germany and United States. Frequent co-authors include Matthias Bethge, Wieland Brendel, Jonas Rauber, Shengdong Zhang, Naveen Ramakrishnan, Mohak Shah, R. Zimmermann, Evgenia Rusak and Oliver Bringmann. Their work appears in journals such as MPG.PuRe (Max Planck Society) 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.