Robert Gens
Impact in
- Artificial Intelligence top 10%
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
- Neural Networks and Applications
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Bayesian Modeling and Causal Inference 2
- Domain Adaptation and Few-Shot Learning 1
- Machine Learning and Algorithms 1
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- Advanced Image and Video Retrieval Techniques 1
- Co-authors
- Pedro Domingos (3 shared papers)Robert Peharz (1 shared paper)Z. Vekerdy (1 shared paper)Christine Pohl (1 shared paper)Ted Selker (1 shared paper)
- Journals
- Neural Information Processing Systems (2 papers)University of Twente Research Information (2 papers)TU/e Research Portal (Eindhoven University of Technology) (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesAustria
In The Last Decade
Robert Gens
6 papers receiving 231 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 180
- Computer Vision and Pattern Recognition 99
- Computational Mathematics 2
- Computational Theory and Mathematics 39
- Signal Processing 25
Countries citing papers authored by Robert Gens
This map shows the geographic impact of Robert Gens'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 Robert Gens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Gens more than expected).
Fields of papers citing papers by Robert Gens
This network shows the impact of papers produced by Robert Gens. 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 Robert Gens. The network helps show where Robert Gens may publish in the future.
Co-authors
The 5 scholars most cited alongside Robert Gens, 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 | Deep Symmetry Networks | 2014 | 77 |
| 2 | Discriminative Learning of Sum-Product Networks | 2012 | 75 |
| 3 | Learning the Structure of Sum-Product Networks | 2013 | 75 |
| 4 | Learning Selective Sum-Product Networks | 2014 | 17 |
| 5 | Image and Data Fusion: Concept and Implementation of a Multimedia Tutorial | 1998 | 3 |
| 6 | Thematic information extraction in a neural network classification of multi sensor data including microwave phase information | 1996 | 2 |
| 7 | 2005 | 0 |
About Robert Gens
Robert Gens is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Information Systems and Computational Theory and Mathematics, having authored 7 papers that have together received 249 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Machine Learning and Algorithms (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Computational Drug Discovery Methods (1 paper), ICT in Developing Communities (1 paper), Mobile and Web Applications (1 paper) and Multi-Criteria Decision Making (1 paper). The work is most often cited by research in Artificial Intelligence (180 citations), Computer Vision and Pattern Recognition (99 citations), Computational Mathematics (2 citations), Computational Theory and Mathematics (39 citations) and Signal Processing (25 citations). Robert Gens has collaborated with scholars based in United States and Austria. Frequent co-authors include Pedro Domingos, Robert Peharz, Z. Vekerdy, Christine Pohl and Ted Selker. Their work appears in journals such as Neural Information Processing Systems, University of Twente Research Information, TU/e Research Portal (Eindhoven University of Technology) 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.