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.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
20191.4k citationsMyle Ott, Sergey Edunov et al.profile →
This map shows the geographic impact of Angela Fan'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 Angela Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Angela Fan more than expected).
This network shows the impact of papers produced by Angela Fan. 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 Angela Fan. The network helps show where Angela Fan may publish in the future.
Co-authorship network of co-authors of Angela Fan
This figure shows the co-authorship network connecting the top 25 collaborators of Angela Fan.
A scholar is included among the top collaborators of Angela Fan 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 Angela Fan. Angela Fan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Scao, Teven Le, Angela Fan, Christopher Akiki, et al.. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. arXiv (Cornell University).174 indexed citations breakdown →
7.
Dua, Dheeru, Shruti Bhosale, Vedanuj Goswami, et al.. (2022). Tricks for Training Sparse Translation Models. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3340–3345.9 indexed citations
Fan, Angela, Édouard Grave, & Armand Joulin. (2020). Reducing Transformer Depth on Demand with Structured Dropout. arXiv (Cornell University).46 indexed citations
11.
Fan, Angela, Pierre Stock, Benjamin Graham, et al.. (2020). Training with Quantization Noise for Extreme Fixed-Point Compression. arXiv (Cornell University).3 indexed citations
12.
Fan, Angela, Aleksandra Piktus, Fabio Petroni, et al.. (2020). Generating Fact Checking Briefs. 7147–7161.21 indexed citations
13.
Fan, Angela, Jack Urbanek, Emily Dinan, et al.. (2020). Generating Interactive Worlds with Text. Proceedings of the AAAI Conference on Artificial Intelligence. 34(2). 1693–1700.7 indexed citations
14.
Ott, Myle, Sergey Edunov, Alexei Baevski, et al.. (2019). fairseq: A Fast, Extensible Toolkit for Sequence Modeling. 48–53.1381 indexed citations breakdown →
Phibbs, Ciaran S., et al.. (2010). Research Guide to Decision Support System National Cost Extracts.18 indexed citations
18.
Chen, Tzeng‐Ji, et al.. (2002). Utilization of psychotropic drugs in Taiwan: an overview of outpatient sector in 2000.. PubMed. 65(8). 378–91.7 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.