Urs Bergmann
Impact in
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- Computer Graphics and Visualization Techniques
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- Advanced Vision and Imaging
- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Advanced Image and Video Retrieval Techniques
Papers in
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- Neural Networks and Applications 2
- Machine Learning and Algorithms 1
- Bayesian Modeling and Causal Inference 1
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- Advanced Image and Video Retrieval Techniques 1
- Co-authors
- Roland Vollgraf (2 shared papers)Nikolay Jetchev (1 shared paper)Noha Radwan (1 shared paper)Thomas Funkhouser (1 shared paper)Klaus Greff (1 shared paper)Mehdi S. M. Sajjadi (1 shared paper)Henning Meyer (1 shared paper)Daniel Duckworth (1 shared paper)
- Journals
- Biological Cybernetics (1 paper)Neural Computation (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- GermanyUnited StatesCanada
In The Last Decade
Urs Bergmann
5 papers receiving 95 citations
Peers
Comparison fields: 5 of 28
- Computer Graphics and Computer-Aided Design 45
- Computer Vision and Pattern Recognition 84
- Computational Mechanics 35
- Museology 5
- Geology 6
Countries citing papers authored by Urs Bergmann
This map shows the geographic impact of Urs Bergmann'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 Urs Bergmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Urs Bergmann more than expected).
Fields of papers citing papers by Urs Bergmann
This network shows the impact of papers produced by Urs Bergmann. 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 Urs Bergmann. The network helps show where Urs Bergmann may publish in the future.
Co-authors
The 18 scholars most cited alongside Urs Bergmann, 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 | 2022 | 66 | |
| 2 | 2019 | 25 | |
| 3 | 2011 | 6 | |
| 4 | Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | 2021 | 2 |
| 5 | 2009 | 1 |
About Urs Bergmann
Urs Bergmann is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Cognitive Neuroscience and Signal Processing, having authored 5 papers that have together received 100 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (2 papers), Neural Networks and Applications (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Cell Image Analysis Techniques (1 paper), Time Series Analysis and Forecasting (1 paper), Machine Learning and Algorithms (1 paper), Bayesian Modeling and Causal Inference (1 paper) and 3D Shape Modeling and Analysis (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (45 citations), Computer Vision and Pattern Recognition (84 citations), Computational Mechanics (35 citations), Museology (5 citations) and Geology (6 citations). Urs Bergmann has collaborated with scholars based in Germany, United States and Canada. Frequent co-authors include Roland Vollgraf, Nikolay Jetchev, Noha Radwan, Thomas Funkhouser, Klaus Greff, Mehdi S. M. Sajjadi, Henning Meyer, Daniel Duckworth, Andrea Tagliasacchi and Etienne Pot. Their work appears in journals such as Biological Cybernetics, Neural Computation, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 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.