John Thickstun

643 total citations
6 papers, 30 citations indexed

About

John Thickstun is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Music. According to data from OpenAlex, John Thickstun has authored 6 papers receiving a total of 30 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Music. Recurrent topics in John Thickstun's work include Topic Modeling (3 papers), Music Technology and Sound Studies (2 papers) and Music and Audio Processing (2 papers). John Thickstun is often cited by papers focused on Topic Modeling (3 papers), Music Technology and Sound Studies (2 papers) and Music and Audio Processing (2 papers). John Thickstun collaborates with scholars based in United States and France. John Thickstun's co-authors include Zaïd Harchaoui, Sham M. Kakade, Sean Welleck, Rowan Zellers, Swabha Swayamdipta, Yejin Choi, Harsh Kumar Verma, John K. Hewitt, Percy Liang and Christopher D. Manning and has published in prestigious journals such as arXiv (Cornell University), International Conference on Learning Representations and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

John Thickstun

6 papers receiving 29 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
John Thickstun United States	3	16	16	14	5	3	6	30
Darius Afchar Austria	2	14 0.9×	19 1.2×	17 1.2×	5 1.0×	15 5.0×	3	38
Jörg Bornschein Germany	5	12 0.8×	13 0.8×	20 1.4×	12 2.4×	1 0.3×	7	44
Pauline Luc United Kingdom	2	16 1.0×	26 1.6×	19 1.4×	2 0.4×	1 0.3×	2	39
Kamel Aloui Tunisia	4	8 0.5×	11 0.7×	7 0.5×	14 2.8×	6 2.0×	15	42
Łukasz Lew United States	2	14 0.9×	8 0.5×	27 1.9×	2 0.4×		3	39
Ishaan Gulrajani United States	2	39 2.4×	24 1.5×	32 2.3×	6 1.2×		5	63
Christoph Wick Germany	4	28 1.8×	16 1.0×	16 1.1×	1 0.2×	1 0.3×	13	41
Jacqueline Pan United Kingdom	3	18 1.1×	6 0.4×	17 1.2×	5 1.0×		3	54
Mohammad Malekzadeh United Kingdom	4	10 0.6×	4 0.3×	30 2.1×	5 1.0×	3 1.0×	9	46
Maciej Szymkowski Poland	4	24 1.5×	23 1.4×	4 0.3×	2 0.4×	7 2.3×	10	60

Countries citing papers authored by John Thickstun

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of John Thickstun

This figure shows the co-authorship network connecting the top 25 collaborators of John Thickstun. A scholar is included among the top collaborators of John Thickstun 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 John Thickstun. John Thickstun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Hewitt, John K., John Thickstun, Christopher D. Manning, & Percy Liang. (2023). Backpack Language Models. 9103–9125. 2 indexed citations

2.

Gulrajani, Ishaan, et al.. (2022). Diffusion-LM Improves Controllable Text Generation. 4328–4343. 1 indexed citations

3.

Swayamdipta, Swabha, et al.. (2021). MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation.. arXiv (Cornell University). 1 indexed citations

4.

Swayamdipta, Swabha, Rowan Zellers, John Thickstun, et al.. (2021). MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. arXiv (Cornell University). 34. 11 indexed citations

5.

Verma, Harsh Kumar & John Thickstun. (2019). Convolutional Composer Classification. Zenodo (CERN European Organization for Nuclear Research). 549–556. 4 indexed citations

6.

Thickstun, John, Zaïd Harchaoui, & Sham M. Kakade. (2016). Learning Features of Music from Scratch. International Conference on Learning Representations. 11 indexed citations

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.

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