International Conference on Learning Representations (ICLR 2013)2013 · 3.6k citations
What are hit papers?
Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if any of the following hold:
it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the
threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.
2013International Conference on Learning Representations (ICLR 2013)
This map shows the geographic impact of 豊 松尾'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 豊 松尾 with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites 豊 松尾 more than expected).
This network shows the impact of papers produced by 豊 松尾. 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 豊 松尾. The network helps show where 豊 松尾 may publish in the future.
豊 松尾 is a scholar working on Statistical and Nonlinear Physics, Information Systems, Artificial Intelligence, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 3.6k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (2 papers), Web Data Mining and Analysis (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Artificial Intelligence (1.7k citations), Signal Processing (296 citations), Computer Graphics and Computer-Aided Design (91 citations) and Media Technology (140 citations). Their work appears in journals such as Harvard business review and Medical Entomology and Zoology.
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research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
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Rankless may not fully capture the entirety of a scholar's output or impact.