Timo Schick
- Artificial Intelligence top 5%
- Topic Modeling 10
- Natural Language Processing Techniques 6
- Domain Adaptation and Few-Shot Learning 4
- Speech and dialogue systems 1
- Hate Speech and Cyberbullying Detection 1
- Machine Learning and Algorithms 1
- Health Informatics top 10%
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- Multimodal Machine Learning Applications 4
- Information Systems top 10%
- Software Engineering Research 2
- General Social Sciences top 10%
- Co-authors
- Hinrich SchützeSahana UdupaHelmut SchmidThomas ScialomOr HonovichOmer LevyΝικόλαος ΑλέτραςXilun Chen
- Journals
- Transactions of the Association for Computational Linguistics (2 papers)Nature Machine Intelligence (1 paper)Open access LMU (Ludwid Maxmilian's Universitat Munchen) (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Timo Schick
13 papers receiving 443 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 412
- Health Informatics 14
- Computer Vision and Pattern Recognition 95
- Information Systems 50
- General Social Sciences 7
Countries citing papers authored by Timo Schick
This map shows the geographic impact of Timo Schick'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 Timo Schick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Schick more than expected).
Fields of papers citing papers by Timo Schick
This network shows the impact of papers produced by Timo Schick. 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 Timo Schick. The network helps show where Timo Schick may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Timo Schick, 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 | 2024 | 1 | |
| 2 | 2023 | 50 | |
| 3 | 2023 | 4 | |
| 4 | 2023 | 16 | |
| 5 | 2023 | 21 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 12 | |
| 8 | 2022 | 6 | |
| 9 | 2022 | 34 | |
| 10 | 2021 | 75 | |
| 11 | 2021 | 137 | |
| 12 | 2020 | 92 | |
| 13 | 2020 | 14 |
About Timo Schick
Timo Schick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Communication, Information Systems and Education, having authored 13 papers that have together received 466 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (6 papers), Multimodal Machine Learning Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Software Engineering Research (2 papers), Speech and dialogue systems (1 paper), Hate Speech and Cyberbullying Detection (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (412 citations), Health Informatics (14 citations), Computer Vision and Pattern Recognition (95 citations), Information Systems (50 citations) and General Social Sciences (7 citations). Timo Schick has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Hinrich Schütze, Sahana Udupa, Helmut Schmid, Thomas Scialom, Or Honovich, Omer Levy, Νικόλαος Αλέτρας, Xilun Chen, Gautier Izacard and Akari Asai. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Nature Machine Intelligence, Open access LMU (Ludwid Maxmilian's Universitat Munchen) 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.