Thomas Lampe

2.1k citations
21 papers · 742 · h-index 11

Impact in

Papers in

Thomas Lampe

21 papers receiving 729 citations

Peers

Thomas Lampe
Comparison fields: 5 of 65
  • Electrical and Electronic Engineering 500
  • Materials Chemistry 342
  • Polymers and Plastics 79
  • Control and Systems Engineering 87
  • Artificial Intelligence 111
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Citations per year

Countries citing papers authored by Thomas Lampe

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Lampe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Thomas Lampe, 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 Thomas Lampe Line = papers co-authored together Thomas Lampe links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015219
2 2017171
3 201778
4 201651
5
Learning by Playing - Solving Sparse Reward Tasks from Scratch
201848
6 201337
7 202032
8 201419
9
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
202017
10 201414
11 200210
12 20169
13 20219
14 20138
15 20157
16 20224
17
Data-efficient Hindsight Off-policy Option Learning
20213
18 20042
19 20192
20 20241

About Thomas Lampe

Thomas Lampe is a scholar working on Artificial Intelligence, Materials Chemistry, Electrical and Electronic Engineering, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 742 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Organic Electronics and Photovoltaics (5 papers), Organic Light-Emitting Diodes Research (4 papers), Luminescence and Fluorescent Materials (4 papers), Robot Manipulation and Learning (4 papers), Adversarial Robustness in Machine Learning (2 papers), Muon and positron interactions and applications (2 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Electrical and Electronic Engineering (500 citations), Materials Chemistry (342 citations), Polymers and Plastics (79 citations), Control and Systems Engineering (87 citations) and Artificial Intelligence (111 citations). Thomas Lampe has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Wolfgang Brütting, Tobias D. Schmidt, Peter I. Djurovich, Mark E. Thompson, Matthew J. Jurow, Martin Riedmiller, Christian Mayr, Daniel Sylvinson Muthiah Ravinson, Roland Hafner and Nicolas Heess. Their work appears in journals such as Nano Letters, Applied Physics Letters, Chemistry of Materials, Nature Materials and ACS Applied Materials & Interfaces.

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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