British Machine Vision Conference (BMVC) (1 paper)IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (1 paper)
fairseq: A Fast, Extensible Toolkit for Sequence Modeling2019 · 1.7k citations
What are hit papers?
Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.
Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.
2019fairseq: A Fast, Extensible Toolkit for Sequence Modeling
This map shows the geographic impact of Sam Gross'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 Sam Gross with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Gross more than expected).
This network shows the impact of papers produced by Sam Gross. 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 Sam Gross. The network helps show where Sam Gross may publish in the future.
Co-authors
The 20 scholars most cited alongside Sam Gross, linked wherever they
have co-authored with each other. Click a name or a connecting line to browse the papers
they share.
Border = papers with Sam GrossLine = papers co-authored togetherSam Gross links everyone, so they are left out of the graph.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings·Sam Gross, Marc’Aurelio Ranzato, Arthur D. Szlam
2017
47
About Sam Gross
Sam Gross is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Nuclear Energy and Engineering, Waste Management and Disposal and Infectious Diseases, having authored 4 papers that have together received 8.9k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Computational Physics and Python Applications (1 paper), Logic, programming, and type systems (1 paper), Topic Modeling (1 paper), Natural Language Processing Techniques (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (3.2k citations), Artificial Intelligence (3.6k citations), Computational Mathematics (21 citations), Hardware and Architecture (224 citations) and Signal Processing (200 citations). Sam Gross has collaborated with scholars based in United States and France. Frequent co-authors include Adam K. Lerer, Soumith Chintala, Edward Z. Yang, Alban Desmaison, Adam Paszke, Zeming Lin, Zachary DeVito, Luca Antiga, Myle Ott and Michael Auli. Their work appears in journals such as British Machine Vision Conference (BMVC) and IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings.
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.