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.
Transfer Learning in Natural Language Processing
2019311 citationsSebastian Ruder, Matthew E. Peters et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Sebastian Ruder
Since
Specialization
Citations
This map shows the geographic impact of Sebastian Ruder'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 Sebastian Ruder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Ruder more than expected).
This network shows the impact of papers produced by Sebastian Ruder. 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 Sebastian Ruder. The network helps show where Sebastian Ruder may publish in the future.
Co-authorship network of co-authors of Sebastian Ruder
This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Ruder.
A scholar is included among the top collaborators of Sebastian Ruder 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 Sebastian Ruder. Sebastian Ruder is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tänzer, Michael, Sebastian Ruder, & Marek Rei. (2022). Memorisation versus Generalisation in Pre-trained Language Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7564–7578.26 indexed citations
Stan, Adriana, Traian Rebedea, Dani Yogatama, et al.. (2021). LiRo: Benchmark and leaderboard for Romanian language tasks. Neural Information Processing Systems.10 indexed citations
Tay, Yi, Mostafa Dehghani, Samira Abnar, et al.. (2021). Long Range Arena : A Benchmark for Efficient Transformers.15 indexed citations
16.
Hu, Junjie, Sebastian Ruder, Aditya Siddhant, et al.. (2020). XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation. International Conference on Machine Learning. 1. 4411–4421.202 indexed citations
17.
Ruder, Sebastian, Matthew E. Peters, Swabha Swayamdipta, & Thomas Wolf. (2019). Transfer Learning in Natural Language Processing. 15–18.311 indexed citations breakdown →
18.
Augenstein, Isabelle, Sebastian Ruder, & Anders Søgaard. (2018). . arXiv (Cornell University).39 indexed citations
19.
Ruder, Sebastian, Joachim Bingel, Isabelle Augenstein, & Anders Søgaard. (2017). Sluice networks: Learning what to share between loosely related tasks.. arXiv (Cornell University).61 indexed citations
20.
Ruder, Sebastian, Joachim Bingel, Isabelle Augenstein, & Anders Søgaard. (2017). Learning what to share between loosely related tasks. Research at the University of Copenhagen (University of Copenhagen).25 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.