Dušan Variš
Impact in
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- Natural Language Processing Techniques
- Topic Modeling
- Text Readability and Simplification
- Speech Recognition and Synthesis
- Speech and dialogue systems
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- Multimodal Machine Learning Applications
- Handwritten Text Recognition Techniques
Papers in
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- Topic Modeling 6
- Natural Language Processing Techniques 5
- Reinforcement Learning in Robotics 1
- Text Readability and Simplification 1
- Speech and dialogue systems 1
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- Multimodal Machine Learning Applications 4
- Co-authors
- Ondřej Bojar (5 shared papers)Jindřich Helcl (2 shared papers)Jindřich Libovický (2 shared papers)Rudolf Rosa (1 shared paper)David Mareček (2 shared papers)Tom Kocmi (2 shared papers)Martin Popel (1 shared paper)
- Journals
- Edinburgh Research Explorer (1 paper)Edinburgh Research Explorer (University of Edinburgh) (1 paper)Americanae (AECID Library) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
In The Last Decade
Dušan Variš
7 papers receiving 67 citations
Peers
Comparison fields: 5 of 22
- Artificial Intelligence 64
- Computer Vision and Pattern Recognition 39
- Health Informatics 1
- Energy Engineering and Power Technology 1
- Software 1
Countries citing papers authored by Dušan Variš
This map shows the geographic impact of Dušan Variš'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 Dušan Variš with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dušan Variš more than expected).
Fields of papers citing papers by Dušan Variš
This network shows the impact of papers produced by Dušan Variš. 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 Dušan Variš. The network helps show where Dušan Variš may publish in the future.
Co-authors
The 7 scholars most cited alongside Dušan Variš, 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 | 2017 | 35 | |
| 2 | 2021 | 16 | |
| 3 | 2017 | 10 | |
| 4 | Neural Monkey: The Current State and Beyond | 2018 | 4 |
| 5 | 2017 | 4 | |
| 6 | 2017 | 3 | |
| 7 | LINDAT Translation service | 2019 | 1 |
About Dušan Variš
Dušan Variš is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Organic Chemistry and Surgery, having authored 7 papers that have together received 73 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Multimodal Machine Learning Applications (4 papers), Reinforcement Learning in Robotics (1 paper), Text Readability and Simplification (1 paper) and Speech and dialogue systems (1 paper). The work is most often cited by research in Artificial Intelligence (64 citations), Computer Vision and Pattern Recognition (39 citations), Health Informatics (1 citation), Energy Engineering and Power Technology (1 citation) and Software (1 citation). Dušan Variš has collaborated with scholars based in Czechia and France. Frequent co-authors include Ondřej Bojar, Jindřich Helcl, Jindřich Libovický, Rudolf Rosa, David Mareček, Tom Kocmi and Martin Popel. Their work appears in journals such as Edinburgh Research Explorer, Edinburgh Research Explorer (University of Edinburgh), Americanae (AECID Library) 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.