Leander Weber
Impact in
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- AI in cancer detection
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Explainable Artificial Intelligence (XAI) 6
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Sebastian Lapuschkin (7 shared papers)Wojciech Samek (5 shared papers)Alexander Binder (3 shared papers)Klaus- Robert Müller (2 shared papers)Christopher J. Anders (1 shared paper)Grégoire Montavon (1 shared paper)Marina Höhne (1 shared paper)Dario Zanca (1 shared paper)
- Journals
- Information Fusion (2 papers)Lecture notes in computer science (1 paper)Communications in computer and information science (1 paper)IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (1 paper)Proceedings - International Conference on Pattern Recognition (1 paper)
In The Last Decade
Leander Weber
7 papers receiving 232 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 174
- Health Informatics 4
- Safety Research 9
- Media Technology 5
- Computer Vision and Pattern Recognition 12
Countries citing papers authored by Leander Weber
This map shows the geographic impact of Leander Weber'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 Leander Weber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leander Weber more than expected).
Fields of papers citing papers by Leander Weber
This network shows the impact of papers produced by Leander Weber. 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 Leander Weber. The network helps show where Leander Weber may publish in the future.
Co-authors
The 8 scholars most cited alongside Leander Weber, 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 | 2022 | 108 | |
| 2 | 2021 | 87 | |
| 3 | 2023 | 15 | |
| 4 | 2021 | 14 | |
| 5 | 2024 | 6 | |
| 6 | 2022 | 3 | |
| 7 | 2022 | 3 |
About Leander Weber
Leander Weber is a scholar working on Artificial Intelligence, Nuclear Energy and Engineering, Waste Management and Disposal, Infectious Diseases and Organic Chemistry, having authored 7 papers that have together received 236 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (6 papers) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Artificial Intelligence (174 citations), Health Informatics (4 citations), Safety Research (9 citations), Media Technology (5 citations) and Computer Vision and Pattern Recognition (12 citations). Leander Weber has collaborated with scholars based in Germany, Singapore and Norway. Frequent co-authors include Sebastian Lapuschkin, Wojciech Samek, Alexander Binder, Klaus- Robert Müller, Christopher J. Anders, Grégoire Montavon, Marina Höhne and Dario Zanca. Their work appears in journals such as Information Fusion, Lecture notes in computer science, Communications in computer and information science, IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings and Proceedings - International Conference on Pattern Recognition.
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.