Hubert Ramsauer
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Digital Media Forensic Detection
- Face recognition and analysis
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
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- Computer Graphics and Visualization Techniques
Papers in
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- Generative Adversarial Networks and Image Synthesis 2
- Face recognition and analysis 1
- Digital Media Forensic Detection 1
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- Neural Networks and Applications 1
- AI in cancer detection 1
- Co-authors
- Sepp Hochreiter (5 shared papers)Thomas Unterthiner (2 shared papers)Bernhard Nessler (2 shared papers)Martin Heusel (2 shared papers)Günter Klambauer (3 shared papers)Lukas Gruber (1 shared paper)Johannes M. Lehner (1 shared paper)Thomas Adler (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- Austria
In The Last Decade
Hubert Ramsauer
4 papers receiving 2.2k citations
Hubert Ramsauer's Hit Papers
Peers
Comparison fields: 5 of 119
- Computer Vision and Pattern Recognition 1.8k
- Computer Graphics and Computer-Aided Design 228
- Media Technology 144
- Artificial Intelligence 548
- Signal Processing 149
Countries citing papers authored by Hubert Ramsauer
This map shows the geographic impact of Hubert Ramsauer'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 Hubert Ramsauer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hubert Ramsauer more than expected).
Fields of papers citing papers by Hubert Ramsauer
This network shows the impact of papers produced by Hubert Ramsauer. 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 Hubert Ramsauer. The network helps show where Hubert Ramsauer may publish in the future.
Co-authors
The 13 scholars most cited alongside Hubert Ramsauer, 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 | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Hit paper breakdown → | 2017 | 2202 |
| 2 | Hopfield Networks is All You Need | 2021 | 81 |
| 3 | Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields | 2018 | 6 |
| 4 | A GAN based solver of black-box inverse problems | 2019 | 1 |
| 5 | 2022 | 0 |
About Hubert Ramsauer
Hubert Ramsauer is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Civil and Structural Engineering and Mathematical Physics, having authored 5 papers that have together received 2.3k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Neural Networks and Applications (1 paper), Topology Optimization in Engineering (1 paper), Face recognition and analysis (1 paper), Digital Media Forensic Detection (1 paper), Matrix Theory and Algorithms (1 paper), COVID-19 diagnosis using AI (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.8k citations), Computer Graphics and Computer-Aided Design (228 citations), Media Technology (144 citations), Artificial Intelligence (548 citations) and Signal Processing (149 citations). Hubert Ramsauer has collaborated with scholars based in Austria. Frequent co-authors include Sepp Hochreiter, Thomas Unterthiner, Bernhard Nessler, Martin Heusel, Günter Klambauer, Lukas Gruber, Johannes M. Lehner, Thomas Adler, J. Brandstetter and Michael Widrich. Their work appears in journals such as arXiv (Cornell University) and International Conference on Learning Representations.
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.