Philipp Seidl
Impact in
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- Neural Networks and Applications
- Topic Modeling
- Neural Networks and Reservoir Computing
- Natural Language Processing Techniques
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Healthcare 2
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- Machine Learning in Materials Science 3
- Co-authors
- Sepp Hochreiter (6 shared papers)Günter Klambauer (3 shared papers)Marwin Segler (2 shared papers)David P. Kreil (1 shared paper)Lukas Gruber (1 shared paper)Hubert Ramsauer (1 shared paper)Thomas Adler (1 shared paper)Michael Kopp (1 shared paper)
- Journals
- European Journal of Emergency Medicine (1 paper)Anesthesia & Analgesia (1 paper)Faraday Discussions (1 paper)Journal of Chemical Information and Modeling (1 paper)Journal of Clinical Anesthesia (1 paper)
- Partner nations
- AustriaUnited KingdomUnited States
In The Last Decade
Philipp Seidl
8 papers receiving 153 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 79
- Computational Theory and Mathematics 38
- Health Informatics 3
- Computer Vision and Pattern Recognition 33
- Materials Chemistry 44
Countries citing papers authored by Philipp Seidl
This map shows the geographic impact of Philipp Seidl'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 Philipp Seidl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Seidl more than expected).
Fields of papers citing papers by Philipp Seidl
This network shows the impact of papers produced by Philipp Seidl. 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 Philipp Seidl. The network helps show where Philipp Seidl may publish in the future.
Co-authors
The 25 scholars most cited alongside Philipp Seidl, 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 | Hopfield Networks is All You Need | 2021 | 77 |
| 2 | 2022 | 46 | |
| 3 | 2024 | 18 | |
| 4 | 2023 | 10 | |
| 5 | 2024 | 2 | |
| 6 | 2022 | 2 | |
| 7 | Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction. | 2021 | 1 |
| 8 | 2024 | 1 |
About Philipp Seidl
Philipp Seidl is a scholar working on Artificial Intelligence, Materials Chemistry, Molecular Biology, Computational Theory and Mathematics and Critical Care and Intensive Care Medicine, having authored 8 papers that have together received 157 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Computational Drug Discovery Methods (2 papers), Machine Learning in Healthcare (2 papers), Machine Learning in Bioinformatics (1 paper), Complex Systems and Time Series Analysis (1 paper), Medical Coding and Health Information (1 paper), Heart Failure Treatment and Management (1 paper) and Emergency and Acute Care Studies (1 paper). The work is most often cited by research in Artificial Intelligence (79 citations), Computational Theory and Mathematics (38 citations), Health Informatics (3 citations), Computer Vision and Pattern Recognition (33 citations) and Materials Chemistry (44 citations). Philipp Seidl has collaborated with scholars based in Austria, United Kingdom and United States. Frequent co-authors include Sepp Hochreiter, Günter Klambauer, Marwin Segler, David P. Kreil, Lukas Gruber, Hubert Ramsauer, Thomas Adler, Michael Kopp, Michael Widrich and Johannes M. Lehner. Their work appears in journals such as European Journal of Emergency Medicine, Anesthesia & Analgesia, Faraday Discussions, Journal of Chemical Information and Modeling and Journal of Clinical Anesthesia.
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.