Victor Sanh

53 total papers · 19.0k total citations
9 papers, 656 citations indexed

About

Victor Sanh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Victor Sanh has authored 9 papers receiving a total of 656 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Computer Networks and Communications. Recurrent topics in Victor Sanh's work include Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (4 papers). Victor Sanh is often cited by papers focused on Topic Modeling (7 papers), Natural Language Processing Techniques (6 papers) and Multimodal Machine Learning Applications (4 papers). Victor Sanh collaborates with scholars based in United States, France and China. Victor Sanh's co-authors include Alexander M. Rush, Teven Le Scao, Quentin Lhoest, Yacine Jernite, Julien Plu, Anthony Moi, Canwen Xu, Lysandre Debut, Sylvain Gugger and Clément Delangue and has published in prestigious journals such as IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

Victor Sanh

8 papers receiving 595 citations

Hit Papers

Transformers: State-of-th... 2020 2026 2022 2024 2020 100 200 300 400

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Victor Sanh 558 146 75 36 28 9 656
Canwen Xu 686 1.2× 193 1.3× 72 1.0× 40 1.1× 12 0.4× 20 752
Yutai Hou 454 0.8× 106 0.7× 67 0.9× 24 0.7× 10 0.4× 16 554
Peng Zhou 545 1.0× 165 1.1× 60 0.8× 18 0.5× 16 0.6× 7 621
Zhiruo Wang 585 1.0× 177 1.2× 72 1.0× 18 0.5× 16 0.6× 11 656
Jonathan Herzig 596 1.1× 150 1.0× 66 0.9× 32 0.9× 8 0.3× 24 715
Oscar Sainz 415 0.7× 65 0.4× 110 1.5× 27 0.8× 25 0.9× 8 649
James Wexler 403 0.7× 210 1.4× 60 0.8× 22 0.6× 18 0.6× 12 629
Alisa Liu 471 0.8× 116 0.8× 68 0.9× 15 0.4× 9 0.3× 10 593
Ilana Heintz 371 0.7× 53 0.4× 100 1.3× 27 0.8× 25 0.9× 9 609
Swaroop Mishra 596 1.1× 168 1.2× 91 1.2× 21 0.6× 47 1.7× 29 765

Countries citing papers authored by Victor Sanh

Since Specialization
Citations

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

Fields of papers citing papers by Victor Sanh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Victor Sanh

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

All Works

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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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