Thomas Wolf
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Speech and dialogue systems
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
Papers in
-
- Topic Modeling 5
- Natural Language Processing Techniques 4
-
- Optical measurement and interference techniques 1
- Human Pose and Action Recognition 1
- Co-authors
- Victor Sanh (2 shared papers)Sebastian Ruder (1 shared paper)Sergey Nikolenko (1 shared paper)Endri Dibra (1 shared paper)Cengiz Öztireli (1 shared paper)Markus Groß (1 shared paper)Aslı Çelikyılmaz (1 shared paper)Yangfeng Ji (1 shared paper)
- Journals
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)Repository for Publications and Research Data (ETH Zurich) (1 paper)MPG.PuRe (Max Planck Society) (1 paper)
- Partner nations
- United StatesGermanySwitzerland
In The Last Decade
Thomas Wolf
9 papers receiving 203 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 164
- Human-Computer Interaction 21
- Computer Vision and Pattern Recognition 64
- Information Systems 24
- Health Informatics 1
Countries citing papers authored by Thomas Wolf
This map shows the geographic impact of Thomas Wolf'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 Wolf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Wolf more than expected).
Fields of papers citing papers by Thomas Wolf
This network shows the impact of papers produced by Thomas Wolf. 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 Wolf. The network helps show where Thomas Wolf may publish in the future.
Co-authors
The 18 scholars most cited alongside Thomas Wolf, 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 | 2019 | 128 | |
| 2 | 2019 | 50 | |
| 3 | 2018 | 16 | |
| 4 | 2020 | 6 | |
| 5 | Learning from others' mistakes: Avoiding dataset biases without modeling them | 2021 | 5 |
| 6 | 2020 | 3 | |
| 7 | 2006 | 2 | |
| 8 | 1996 | 1 | |
| 9 | 2024 | 1 |
About Thomas Wolf
Thomas Wolf is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Control and Systems Engineering and Information Systems, having authored 9 papers that have together received 212 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers), Hand Gesture Recognition Systems (1 paper), Optical measurement and interference techniques (1 paper), Gene expression and cancer classification (1 paper), Bioinformatics and Genomic Networks (1 paper), Human Pose and Action Recognition (1 paper) and Robot Manipulation and Learning (1 paper). The work is most often cited by research in Artificial Intelligence (164 citations), Human-Computer Interaction (21 citations), Computer Vision and Pattern Recognition (64 citations), Information Systems (24 citations) and Health Informatics (1 citation). Thomas Wolf has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Victor Sanh, Sebastian Ruder, Sergey Nikolenko, Endri Dibra, Cengiz Öztireli, Markus Groß, Aslı Çelikyılmaz, Yangfeng Ji, Alex Wang and Antoine Bosselut. Their work appears in journals such as Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, Repository for Publications and Research Data (ETH Zurich) and MPG.PuRe (Max Planck Society).
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.