Thomas Schnake
Impact in
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Advanced Graph Neural Networks
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Topic Modeling
Papers in
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- Topic Modeling 3
- Explainable Artificial Intelligence (XAI) 2
- Anomaly Detection Techniques and Applications 1
- AI in cancer detection 1
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- Network Security and Intrusion Detection 1
- Co-authors
- Grégoire Montavon (3 shared papers)Jonas Lederer (3 shared papers)Shinichi Nakajima (2 shared papers)Oliver Eberle (2 shared papers)Klaus- Robert Müller (3 shared papers)Kristof Sch utt (1 shared paper)Yury Zemlyanskiy (1 shared paper)Robert A. Vandermeulen (1 shared paper)
- Journals
- Journal of Chemical Theory and Computation (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Chaos An Interdisciplinary Journal of Nonlinear Science (1 paper)Information Fusion (1 paper)
- Partner nations
- GermanyUnited StatesSouth Korea
In The Last Decade
Thomas Schnake
6 papers receiving 235 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 170
- Signal Processing 19
- Computer Networks and Communications 37
- Information Systems and Management 10
- Computational Theory and Mathematics 23
Countries citing papers authored by Thomas Schnake
This map shows the geographic impact of Thomas Schnake'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 Schnake with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Schnake more than expected).
Fields of papers citing papers by Thomas Schnake
This network shows the impact of papers produced by Thomas Schnake. 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 Schnake. The network helps show where Thomas Schnake may publish in the future.
Co-authors
The 17 scholars most cited alongside Thomas Schnake, 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 | 2021 | 185 | |
| 2 | 2019 | 38 | |
| 3 | 2025 | 9 | |
| 4 | 2025 | 6 | |
| 5 | 2024 | 1 | |
| 6 | 2025 | 1 |
About Thomas Schnake
Thomas Schnake is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems and Management, Computational Theory and Mathematics and Materials Chemistry, having authored 6 papers that have together received 240 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Explainable Artificial Intelligence (XAI) (2 papers), Machine Learning in Materials Science (2 papers), Computational Drug Discovery Methods (1 paper), Network Security and Intrusion Detection (1 paper), Anomaly Detection Techniques and Applications (1 paper), Scientific Computing and Data Management (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Artificial Intelligence (170 citations), Signal Processing (19 citations), Computer Networks and Communications (37 citations), Information Systems and Management (10 citations) and Computational Theory and Mathematics (23 citations). Thomas Schnake has collaborated with scholars based in Germany, United States and South Korea. Frequent co-authors include Grégoire Montavon, Jonas Lederer, Shinichi Nakajima, Oliver Eberle, Klaus- Robert Müller, Kristof Sch utt, Yury Zemlyanskiy, Robert A. Vandermeulen, Lukas Ruff and Marius Kloft. Their work appears in journals such as Journal of Chemical Theory and Computation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Chaos An Interdisciplinary Journal of Nonlinear Science and Information Fusion.
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.