Patrick Pletscher
Impact in
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- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Stochastic Gradient Optimization Techniques
- Bayesian Modeling and Causal Inference
- Domain Adaptation and Few-Shot Learning
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- Face and Expression Recognition
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Machine Learning and Data Classification 3
- Machine Learning and Algorithms 2
- Stochastic Gradient Optimization Techniques 2
- Bayesian Modeling and Causal Inference 1
- Neural Networks and Applications 1
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- Advanced Image and Video Retrieval Techniques 3
- Face recognition and analysis 1
- Co-authors
- Pushmeet Kohli (1 shared paper)M. Uhr (1 shared paper)Simon Lacoste-Julien (2 shared papers)Martin Jaggi (2 shared papers)Isabelle Guyon (1 shared paper)Matthew Brand (1 shared paper)Mark Schmidt (1 shared paper)Cheng Soon Ong (2 shared papers)
- Journals
- Pattern Recognition Letters (1 paper)Infoscience (Ecole Polytechnique Fédérale de Lausanne) (1 paper)arXiv (Cornell University) (2 papers)International Conference on Artificial Intelligence and Statistics (2 papers)
- Partner nations
- SwitzerlandUnited StatesFrance
In The Last Decade
Patrick Pletscher
7 papers receiving 112 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 90
- Computer Vision and Pattern Recognition 55
- Computational Mathematics 1
- Statistics and Probability 7
- Numerical Analysis 4
Countries citing papers authored by Patrick Pletscher
This map shows the geographic impact of Patrick Pletscher'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 Patrick Pletscher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Pletscher more than expected).
Fields of papers citing papers by Patrick Pletscher
This network shows the impact of papers produced by Patrick Pletscher. 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 Patrick Pletscher. The network helps show where Patrick Pletscher may publish in the future.
Co-authors
The 10 scholars most cited alongside Patrick Pletscher, 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 | 2007 | 46 | |
| 2 | Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | 2013 | 24 |
| 3 | Learning Low-order Models for Enforcing High-order Statistics | 2012 | 17 |
| 4 | 2008 | 15 | |
| 5 | Spanning Tree Approximations for Conditional Random Fields | 2009 | 11 |
| 6 | Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | 2012 | 9 |
| 7 | 2012 | 2 | |
| 8 | Part & Clamp: Efficient Structured Output Learning. | 2012 | 1 |
About Patrick Pletscher
Patrick Pletscher is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Molecular Biology and Information Systems, having authored 8 papers that have together received 125 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (3 papers), Machine Learning and Data Classification (3 papers), Machine Learning and Algorithms (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Error Correcting Code Techniques (1 paper), Bayesian Modeling and Causal Inference (1 paper), Face recognition and analysis (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (90 citations), Computer Vision and Pattern Recognition (55 citations), Computational Mathematics (1 citation), Statistics and Probability (7 citations) and Numerical Analysis (4 citations). Patrick Pletscher has collaborated with scholars based in Switzerland, United States and France. Frequent co-authors include Pushmeet Kohli, M. Uhr, Simon Lacoste-Julien, Martin Jaggi, Isabelle Guyon, Matthew Brand, Mark Schmidt, Cheng Soon Ong, Joachim M. Buhmann and Mark Schmidt. Their work appears in journals such as Pattern Recognition Letters, Infoscience (Ecole Polytechnique Fédérale de Lausanne), arXiv (Cornell University) and International Conference on Artificial Intelligence and Statistics.
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.