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.
Aggregated Residual Transformations for Deep Neural Networks
20177.2k citationsSaining Xie, Ross Girshick et al.profile →
Similarity network fusion for aggregating data types on a genomic scale
This map shows the geographic impact of Zhuowen Tu'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 Zhuowen Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhuowen Tu more than expected).
This network shows the impact of papers produced by Zhuowen Tu. 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 Zhuowen Tu. The network helps show where Zhuowen Tu may publish in the future.
Co-authorship network of co-authors of Zhuowen Tu
This figure shows the co-authorship network connecting the top 25 collaborators of Zhuowen Tu.
A scholar is included among the top collaborators of Zhuowen Tu 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 Zhuowen Tu. Zhuowen Tu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xu, Weijian, et al.. (2020). Attentional Constellation Nets for Few-Shot Learning.. International Conference on Learning Representations.22 indexed citations
Xie, Saining, Ross Girshick, Piotr Dollár, Zhuowen Tu, & Kaiming He. (2017). Aggregated Residual Transformations for Deep Neural Networks. 5987–5995.7181 indexed citations breakdown →
12.
Belghith, Akram, Siamak Yousefi, Jameson Merkow, et al.. (2016). Diabetic retinopathy detection from image to classification using deep convolutional neural network. Investigative Ophthalmology & Visual Science. 57(12). 5961–5961.3 indexed citations
13.
Zhao, Li-Ming, Jingdong Wang, Xi Li, Zhuowen Tu, & Wenjun Zeng. (2016). On the Connection of Deep Fusion to Ensembling.. arXiv (Cornell University).23 indexed citations
Bi, Wei, Liwei Wang, James T. Kwok, & Zhuowen Tu. (2014). Learning to predict from crowdsourced data. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 82–91.31 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.