Victor Sanh

18.9k total citations · 1 hit paper
9 papers, 661 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 661 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 Austria. Victor Sanh's co-authors include Alexander M. Rush, Teven Le Scao, Canwen Xu, Thomas Wolf, Quentin Lhoest, Yacine Jernite, Julien Chaumond, Lysandre Debut, Anthony Moi and Julien Plu 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

9 papers receiving 600 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Transformers: State-of-the-Art Natural Language Processing

2020 436 citations Thomas Wolf, Lysandre Debut et al. Zenodo (CERN European Organization for Nuclear Research) profile →

Peers

Victor Sanh

AI

CVPR

IS

SPS

EEE

Canwen Xu United States

Damai Dai China

Swaroop Mishra United States

Teven Le Scao United States

Yutai Hou China

Zhiruo Wang United States

Yifei Zhang China

Alisa Liu United States

Bing Yin United States

Canwen Xu United States

Victor Sanh

691 ×1.2

AI

193 ×1.3

CVPR

73 ×1.0

IS

40 ×1.1

SPS

12 ×0.4

EEE

Citations per year, relative to Victor Sanh Victor Sanh (= 1×) peers Canwen Xu

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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9 of 9 papers shown

1.

Cord, Matthieu, et al.. (2024). What matters when building vision-language models?. 87874–87907. 1 indexed citations

2.

Scao, Teven Le, Thomas J. Wang, Daniel Hesslow, et al.. (2022). What Language Model to Train if You Have One Million GPU Hours?. 765–782. 18 indexed citations

3.

Strobelt, Hendrik, Albert Webson, Victor Sanh, et al.. (2022). Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models. IEEE Transactions on Visualization and Computer Graphics. 29(1). 1–11. 108 indexed citations

4.

Sanh, Victor, Thomas Wolf, Yonatan Belinkov, & Alexander M. Rush. (2021). Learning from others' mistakes: Avoiding dataset biases without modeling them. 5 indexed citations

5.

Utama, Prasetya Ajie, Nafise Sadat Moosavi, Victor Sanh, & Iryna Gurevych. (2021). Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 9063–9074. 16 indexed citations

6.

Cao, Steven, Victor Sanh, & Alexander M. Rush. (2021). Low-Complexity Probing via Finding Subnetworks. 960–966. 10 indexed citations

7.

Tambe, Thierry, Coleman Hooper, Lillian Pentecost, et al.. (2021). EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference. 830–844. 60 indexed citations

8.

Wolf, Thomas, Lysandre Debut, Victor Sanh, et al.. (2020). Transformers: State-of-the-Art Natural Language Processing. Zenodo (CERN European Organization for Nuclear Research). 436 indexed citations breakdown →

9.

Tambe, Thierry, Coleman Hooper, Lillian Pentecost, et al.. (2020). EdgeBERT: Optimizing On-Chip Inference for Multi-Task NLP.. 7 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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