Markus Wulfmeier

1.3k citations
10 papers · 232 · h-index 5

Impact in

Papers in

Markus Wulfmeier

10 papers receiving 225 citations

Peers

Markus Wulfmeier
Comparison fields: 5 of 52
  • Automotive Engineering 67
  • Computer Vision and Pattern Recognition 79
  • Artificial Intelligence 82
  • Control and Systems Engineering 56
  • Civil and Structural Engineering 43
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Citations per field
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Citations per year

Countries citing papers authored by Markus Wulfmeier

Since Specialization
Citations

This map shows the geographic impact of Markus Wulfmeier'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 Markus Wulfmeier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Wulfmeier more than expected).

Fields of papers citing papers by Markus Wulfmeier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Wulfmeier. 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 Markus Wulfmeier. The network helps show where Markus Wulfmeier may publish in the future.

Co-authors

The 25 scholars most cited alongside Markus Wulfmeier, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Markus Wulfmeier Line = papers co-authored together Markus Wulfmeier links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 2017123
2 201733
3 201332
4
Deep Inverse Reinforcement Learning.
201521
5 201215
6
Data-efficient Hindsight Off-policy Option Learning
20213
7
TACO: Learning Task Decomposition via Temporal Alignment for Control
20182
8 20241
9
Attention Privileged Reinforcement Learning for Domain Transfer
20191
10
Incremental Adversarial Domain Adaptation.
20171

About Markus Wulfmeier

Markus Wulfmeier is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Civil and Structural Engineering, Mechanical Engineering and Automotive Engineering, having authored 10 papers that have together received 232 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Adversarial Robustness in Machine Learning (3 papers), Machine Learning and Data Classification (2 papers), Soil Mechanics and Vehicle Dynamics (2 papers), Agricultural Engineering and Mechanization (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Transportation Safety and Impact Analysis (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Automotive Engineering (67 citations), Computer Vision and Pattern Recognition (79 citations), Artificial Intelligence (82 citations), Control and Systems Engineering (56 citations) and Civil and Structural Engineering (43 citations). Markus Wulfmeier has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Ingmar Posner, Peter Ondrúška, Dushyant Rao, Dominic Zeng Wang, Alex Bewley, Carmine Senatore, Karl Iagnemma, Ivan Vlahinić, José E. Andrade and Paramsothy Jayakumar. Their work appears in journals such as Journal of Terramechanics, The International Journal of Robotics Research, SAE technical papers on CD-ROM/SAE technical paper series, Oxford University Research Archive (ORA) (University of Oxford) 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.

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