Tobias Domhan
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics
- Electrical and Electronic Engineering
- Information Systems
- Co-authors
- Frank HutterJost Tobias SpringenbergFelix HieberBrian J. ThompsonKe TranEva HaslerMarcello FedericoBill Byrne
- Topics
- Natural Language Processing Techniques (5 papers)Topic Modeling (5 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- FreiDok plus (Universitätsbibliothek Freiburg)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Tobias Domhan
7 papers receiving 278 citations
Peers
Comparison fields: 5 of 67
- Artificial Intelligence 216
- Computer Vision and Pattern Recognition 122
- Computational Theory and Mathematics 27
- Electrical and Electronic Engineering 21
- Information Systems 17
Countries citing papers authored by Tobias Domhan
This map shows the geographic impact of Tobias Domhan'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 Tobias Domhan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tobias Domhan more than expected).
Fields of papers citing papers by Tobias Domhan
This network shows the impact of papers produced by Tobias Domhan. 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 Tobias Domhan. The network helps show where Tobias Domhan may publish in the future.
Co-authorship network of co-authors of Tobias Domhan
This figure shows the co-authorship network connecting the top 25 collaborators of Tobias Domhan. A scholar is included among the top collaborators of Tobias Domhan 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 Tobias Domhan. Tobias Domhan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 40 | |
| 6 | 38 | |
| 7 | Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves | 200 |
| 8 | Extrapolating Learning Curves of Deep Neural Networks | 8 |
About Tobias Domhan
Tobias Domhan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 8 papers that have together received 297 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Artificial Intelligence (216 citations), Computer Vision and Pattern Recognition (122 citations) and Health Informatics (4 citations). Tobias Domhan has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Frank Hutter, Jost Tobias Springenberg, Felix Hieber, Brian J. Thompson, Ke Tran, Eva Hasler, Marcello Federico, Bill Byrne, Huda Khayrallah and Rico Sennrich. Their work appears in journals such as FreiDok plus (Universitätsbibliothek Freiburg) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.