Joschka Boedecker

3.3k citations
56 papers · 1.3k indexed · h-index 17
Topics
Reinforcement Learning in Robotics (17 papers)Neural dynamics and brain function (11 papers)Autonomous Vehicle Technology and Safety (8 papers)
Journals
SHILAP Revista de lepidopterologíaPLoS ONENeuroImage
Partner nations
GermanyJapanAustralia

In The Last Decade

Joschka Boedecker

53 papers receiving 1.3k citations

Peers

Joschka Boedecker
Comparison fields: 5 of 114
  • Artificial Intelligence 577
  • Cognitive Neuroscience 346
  • Computer Vision and Pattern Recognition 287
  • Control and Systems Engineering 286
  • Automotive Engineering 165
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Citations per field
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Citations per year

Countries citing papers authored by Joschka Boedecker

Since Specialization
Citations

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

Fields of papers citing papers by Joschka Boedecker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joschka Boedecker

This figure shows the co-authorship network connecting the top 25 collaborators of Joschka Boedecker. A scholar is included among the top collaborators of Joschka Boedecker based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joschka Boedecker. Joschka Boedecker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 2
3 0
4 2
5 5
6 5
7 2
8 5
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10 26
11
AnyNets: Adaptive Deep Neural Networks for Medical Data with Missing Values.
0
12
Interpretable Multi Time-scale Constraints in Model-free Deep Reinforcement Learning for Autonomous Driving.
1
13 57
14
Controlling biological neural networks with deep reinforcement learning.
1
15
Neural SLAM
7
16
Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning
28
17 13
18
Between frustration and elation: sense of control regulates the lntrinsic motivation for motor learning
3
19 162
20
Studies on reservoir initialization and dynamics shaping in echo state networks.
11

About Joschka Boedecker

Joschka Boedecker is a scholar working on Health Informatics, Artificial Intelligence and Cognitive Neuroscience, having authored 56 papers that have together received 1.3k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (17 papers), Neural dynamics and brain function (11 papers) and Autonomous Vehicle Technology and Safety (8 papers). The work is most often cited by research in Artificial Intelligence (577 citations), Cognitive Neuroscience (346 citations) and Health Informatics (20 citations). Joschka Boedecker has collaborated with scholars based in Germany, Japan and Australia. Frequent co-authors include Jost Tobias Springenberg, Wolfram Burgard, Martin Riedmiller, Jingwei Zhang, Minoru Asada, Oliver Obst, N. Michael Mayer, Manuel Watter, Joseph T. Lizier and Moritz Werling. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and NeuroImage.

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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