Yale Song

40 papers and 1.7k indexed citations i.

About

Yale Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Yale Song has authored 40 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 21 papers in Artificial Intelligence and 11 papers in Signal Processing. Recurrent topics in Yale Song’s work include Multimodal Machine Learning Applications (17 papers), Human Pose and Action Recognition (17 papers) and Domain Adaptation and Few-Shot Learning (10 papers). Yale Song is often cited by papers focused on Multimodal Machine Learning Applications (17 papers), Human Pose and Action Recognition (17 papers) and Domain Adaptation and Few-Shot Learning (10 papers). Yale Song collaborates with scholars based in United States, United Kingdom and South Korea. Yale Song's co-authors include Randall Davis, Liangliang Cao, Mohammad Soleymani, Gunhee Kim, Youngjae Yu, Yunseok Jang, Yuncheng Li, Jiebo Luo, Youngjin Kim and Louis‐Philippe Morency and has published in prestigious journals such as International Journal of Computer Vision, IEEE Transactions on Visualization and Computer Graphics and ACM Transactions on Interactive Intelligent Systems.

In The Last Decade

Co-authorship network of co-authors of Yale Song i

Fields of papers citing papers by Yale Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yale Song. 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 Yale Song. The network helps show where Yale Song may publish in the future.

Countries citing papers authored by Yale Song

Since Specialization
Citations

This map shows the geographic impact of Yale Song'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 Yale Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yale Song more than expected).

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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