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.
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
2016379 citationsWongun Choi, Yuanqing Lin et al.profile →
Automated structural design of shear wall residential buildings using generative adversarial networks
2021152 citationsWenjie Liao, Xinzheng Lu et al.Automation in Constructionprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Yuanqing Lin'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 Yuanqing Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuanqing Lin more than expected).
This network shows the impact of papers produced by Yuanqing Lin. 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 Yuanqing Lin. The network helps show where Yuanqing Lin may publish in the future.
Co-authorship network of co-authors of Yuanqing Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Yuanqing Lin.
A scholar is included among the top collaborators of Yuanqing Lin 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 Yuanqing Lin. Yuanqing Lin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Qian, Qi, Rong Jin, Shenghuo Zhu, & Yuanqing Lin. (2014). An Integrated Framework for High Dimensional Distance Metric Learning and Its Application to Fine-Grained Visual Categorization.. arXiv (Cornell University).5 indexed citations
10.
Yang, Tianbao, Shenghuo Zhu, Rong Jin, & Yuanqing Lin. (2013). On Theoretical Analysis of Distributed Stochastic Dual Coordinate Ascent.. arXiv (Cornell University).2 indexed citations
Lin, Yuanqing, Tong Zhang, Shenghuo Zhu, & Kai Yu. (2010). Deep Coding Network. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 23. 1405–1413.17 indexed citations
14.
Lin, Yuanqing, Shenghuo Zhu, Daniel J. Lee, & Ben Taskar. (2009). Learning Sparse Markov Network Structure via Ensemble-of-Trees Models. International Conference on Artificial Intelligence and Statistics. 360–367.7 indexed citations
15.
Lin, Yuanqing, Jingdong Chen, Youngmoo E. Kim, & Daniel D. Lee. (2007). Blind channel identification for speech dereverberation using l1-norm sparse learning. Neural Information Processing Systems. 20. 921–928.27 indexed citations
Lin, Yuanqing & Daniel D. Lee. (2004). Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation. ScholarlyCommons (University of Pennsylvania). 17. 809–816.8 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.