Jörg Bornschein
Impact in
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- Blind Source Separation Techniques
- Speech and Audio Processing
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- Gaussian Processes and Bayesian Inference
Papers in
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- Gaussian Processes and Bayesian Inference 3
- Machine Learning and Algorithms 2
- Computational Physics and Python Applications 1
- Neural Networks and Applications 1
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- Generative Adversarial Networks and Image Synthesis 3
- Image and Signal Denoising Methods 1
- Advanced Image Processing Techniques 1
- Co-authors
- Jörg Lücke (2 shared papers)Francesco Visin (1 shared paper)Asja Fischer (2 shared papers)Simon Osindero (1 shared paper)Yoshua Bengio (2 shared papers)Andriy Mnih (1 shared paper)Daniel Zoran (1 shared paper)Danilo Jimenez Rezende (1 shared paper)
- Journals
- PLoS Computational Biology (1 paper)PLoS ONE (1 paper)arXiv (Cornell University) (2 papers)Neural Information Processing Systems (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- GermanyCanadaUnited States
In The Last Decade
Jörg Bornschein
6 papers receiving 43 citations
Peers
Comparison fields: 5 of 22
- Signal Processing 13
- Artificial Intelligence 20
- Cognitive Neuroscience 12
- Computer Vision and Pattern Recognition 12
- Statistics and Probability 3
Countries citing papers authored by Jörg Bornschein
This map shows the geographic impact of Jörg Bornschein'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 Jörg Bornschein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jörg Bornschein more than expected).
Fields of papers citing papers by Jörg Bornschein
This network shows the impact of papers produced by Jörg Bornschein. 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 Jörg Bornschein. The network helps show where Jörg Bornschein may publish in the future.
Co-authors
The 8 scholars most cited alongside Jörg Bornschein, 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 | 2013 | 14 | |
| 2 | 2015 | 9 | |
| 3 | Bidirectional Helmholtz machines | 2016 | 6 |
| 4 | 2020 | 6 | |
| 5 | Approximate EM Learning on Large Computer Clusters | 2010 | 5 |
| 6 | Variational Memory Addressing in Generative Models | 2017 | 4 |
| 7 | Training opposing directed models using geometric mean matching. | 2015 | 0 |
About Jörg Bornschein
Jörg Bornschein is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Cognitive Neuroscience and Computational Mechanics, having authored 7 papers that have together received 44 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Gaussian Processes and Bayesian Inference (3 papers), Machine Learning and Algorithms (2 papers), Computational Physics and Python Applications (1 paper), Neural Networks and Applications (1 paper), Image and Signal Denoising Methods (1 paper), Advanced Image Processing Techniques (1 paper) and Blind Source Separation Techniques (1 paper). The work is most often cited by research in Signal Processing (13 citations), Artificial Intelligence (20 citations), Cognitive Neuroscience (12 citations), Computer Vision and Pattern Recognition (12 citations) and Statistics and Probability (3 citations). Jörg Bornschein has collaborated with scholars based in Germany, Canada and United States. Frequent co-authors include Jörg Lücke, Francesco Visin, Asja Fischer, Simon Osindero, Yoshua Bengio, Andriy Mnih, Daniel Zoran and Danilo Jimenez Rezende. Their work appears in journals such as PLoS Computational Biology, PLoS ONE, arXiv (Cornell University), Neural Information Processing Systems and International Conference on Machine Learning.
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.