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.
A ConvNet for the 2020s
20223.7k citationsZhuang Liu, Hanzi Mao et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
2022424 citationsYanghao Li, Chao-Yuan Wu et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Recurrent Recommender Networks
2017416 citationsChao-Yuan Wu, Amr Ahmed et al.profile →
Masked Feature Prediction for Self-Supervised Visual Pre-Training
2022324 citationsChen Wei, Haoqi Fan et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
2014297 citationsQiming Diao, Minghui Qiu et al.profile →
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 Chao-Yuan Wu'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 Chao-Yuan Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chao-Yuan Wu more than expected).
This network shows the impact of papers produced by Chao-Yuan Wu. 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 Chao-Yuan Wu. The network helps show where Chao-Yuan Wu may publish in the future.
Co-authorship network of co-authors of Chao-Yuan Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Chao-Yuan Wu.
A scholar is included among the top collaborators of Chao-Yuan Wu 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 Chao-Yuan Wu. Chao-Yuan Wu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Zhuang, Hanzi Mao, Chao-Yuan Wu, et al.. (2022). A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11966–11976.3721 indexed citations breakdown →
Lei, Qi, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, & Pradeep Ravikumar. (2017). Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization.. International Conference on Machine Learning. 2034–2042.2 indexed citations
13.
Wu, Chao-Yuan, Amr Ahmed, Alex Beutel, & Alexander J. Smola. (2017). Joint Training of Ratings and Reviews with Recurrent Recommender Networks. International Conference on Learning Representations.15 indexed citations
14.
Wu, Chao-Yuan, Amr Ahmed, Alex Beutel, Alexander J. Smola, & How Jing. (2017). Recurrent Recommender Networks. 495–503.416 indexed citations breakdown →
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.