Tristan Thrush

591 total citations
8 papers, 187 citations indexed

About

Tristan Thrush is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Sociology and Political Science. According to data from OpenAlex, Tristan Thrush has authored 8 papers receiving a total of 187 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Sociology and Political Science. Recurrent topics in Tristan Thrush's work include Multimodal Machine Learning Applications (3 papers), Natural Language Processing Techniques (3 papers) and Topic Modeling (3 papers). Tristan Thrush is often cited by papers focused on Multimodal Machine Learning Applications (3 papers), Natural Language Processing Techniques (3 papers) and Topic Modeling (3 papers). Tristan Thrush collaborates with scholars based in Israel, United Kingdom and Canada. Tristan Thrush's co-authors include Douwe Kiela, Max Bartolo, Adina Williams, Amanpreet Singh, Candace Ross, Robin Jia, Sebastian Riedel, Pontus Stenetorp, Paul Röttger and Hannah Rose Kirk and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and Lirias (KU Leuven).

In The Last Decade

Tristan Thrush

7 papers receiving 177 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tristan Thrush Israel	6	151	98	11	9	7	8	187
Jan Buys United Kingdom	6	243 1.6×	75 0.8×	26 2.4×	8 0.9×	3 0.4×	19	265
Vincent Claveau France	9	119 0.8×	51 0.5×	15 1.4×	10 1.1×	4 0.6×	39	185
Sosuke Kobayashi Japan	8	119 0.8×	86 0.9×	14 1.3×	6 0.7×	2 0.3×	15	187
Oksana Yakhnenko United States	7	77 0.5×	94 1.0×	8 0.7×	18 2.0×	4 0.6×	8	152
Candace Ross Canada	4	105 0.7×	85 0.9×	3 0.3×	3 0.3×	2 0.3×	7	146
Yunlong Liang China	6	187 1.2×	32 0.3×	27 2.5×	5 0.6×	3 0.4×	17	229
Jiangtao Feng China	6	161 1.1×	59 0.6×	4 0.4×	5 0.6×	3 0.4×	19	190
Tong Niu United States	6	199 1.3×	44 0.4×	13 1.2×	12 1.3×		14	220
Xiaoxi Mao China	9	199 1.3×	55 0.6×	13 1.2×	13 1.4×		14	213
Gustavo Aguilar United States	4	190 1.3×	50 0.5×	12 1.1×	8 0.9×		9	218

Countries citing papers authored by Tristan Thrush

Since Specialization

Citations

This map shows the geographic impact of Tristan Thrush'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 Tristan Thrush with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tristan Thrush more than expected).

Fields of papers citing papers by Tristan Thrush

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tristan Thrush. 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 Tristan Thrush. The network helps show where Tristan Thrush may publish in the future.

Co-authorship network of co-authors of Tristan Thrush

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

All Works

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8 of 8 papers shown

1.

Thrush, Tristan, et al.. (2024). I am a Strange Dataset: Metalinguistic Tests for Language Models. 8888–8907.

2.

Thrush, Tristan, Max Bartolo, Amanpreet Singh, et al.. (2022). Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5228–5238. 98 indexed citations

3.

Tunstall, Lewis, et al.. (2022). Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements. 128–136. 2 indexed citations

4.

Kirk, Hannah Rose, Bertie Vidgen, Paul Röttger, Tristan Thrush, & Scott A. Hale. (2022). Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1352–1368. 24 indexed citations

5.

Bartolo, Max, Tristan Thrush, Sebastian Riedel, et al.. (2022). Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 9 indexed citations

6.

Thrush, Tristan, Kushal Tirumala, Anmol Gupta, et al.. (2022). Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks. 174–181. 5 indexed citations

7.

Pham, Tu-Hoa, Shreyansh Daftry, Barry Ridge, et al.. (2021). Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching. Lirias (KU Leuven). 6 indexed citations

8.

Bartolo, Max, Tristan Thrush, Robin Jia, et al.. (2021). Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation. arXiv (Cornell University). 43 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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