John Thickstun
Impact in
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- Music and Audio Processing
- Speech and Audio Processing
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- Diverse Musicological Studies
Papers in
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- Topic Modeling 3
- Natural Language Processing Techniques 2
- Machine Learning and Data Classification 1
- Explainable Artificial Intelligence (XAI) 1
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- Music Technology and Sound Studies 2
- Co-authors
- Zaïd Harchaoui (3 shared papers)Sham M. Kakade (1 shared paper)Swabha Swayamdipta (2 shared papers)Harsh Kumar Verma (1 shared paper)Rowan Zellers (2 shared papers)Sean Welleck (1 shared paper)Yejin Choi (2 shared papers)John K. Hewitt (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesFrance
In The Last Decade
John Thickstun
6 papers receiving 29 citations
Peers
Comparison fields: 5 of 14
- Signal Processing 16
- Music 3
- Computer Vision and Pattern Recognition 16
- Health Informatics 1
- Artificial Intelligence 14
Countries citing papers authored by John Thickstun
This map shows the geographic impact of John Thickstun'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 John Thickstun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Thickstun more than expected).
Fields of papers citing papers by John Thickstun
This network shows the impact of papers produced by John Thickstun. 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 John Thickstun. The network helps show where John Thickstun may publish in the future.
Co-authors
The 11 scholars most cited alongside John Thickstun, 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 | Learning Features of Music from Scratch | 2016 | 11 |
| 2 | 2021 | 11 | |
| 3 | 2019 | 4 | |
| 4 | 2023 | 2 | |
| 5 | MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. | 2021 | 1 |
| 6 | 2022 | 1 |
About John Thickstun
John Thickstun is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Music, Signal Processing and Information Systems, having authored 6 papers that have together received 30 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (2 papers), Music Technology and Sound Studies (2 papers), Music and Audio Processing (2 papers), Diverse Musicological Studies (2 papers), Software Engineering Research (1 paper), Machine Learning and Data Classification (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Signal Processing (16 citations), Music (3 citations), Computer Vision and Pattern Recognition (16 citations), Health Informatics (1 citation) and Artificial Intelligence (14 citations). John Thickstun has collaborated with scholars based in United States and France. Frequent co-authors include Zaïd Harchaoui, Sham M. Kakade, Swabha Swayamdipta, Harsh Kumar Verma, Rowan Zellers, Sean Welleck, Yejin Choi, John K. Hewitt, Christopher D. Manning and Percy Liang. Their work appears in journals such as International Conference on Learning Representations, Zenodo (CERN European Organization for Nuclear Research) and arXiv (Cornell University).
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.