This map shows the geographic impact of Kai Zhong'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 Kai Zhong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Zhong more than expected).
This network shows the impact of papers produced by Kai Zhong. 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 Kai Zhong. The network helps show where Kai Zhong may publish in the future.
Co-authorship network of co-authors of Kai Zhong
This figure shows the co-authorship network connecting the top 25 collaborators of Kai Zhong.
A scholar is included among the top collaborators of Kai Zhong 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 Kai Zhong. Kai Zhong is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chang, Wei-Cheng, Hsiang‐Fu Yu, Kai Zhong, Yiming Yang, & Inderjit S. Dhillon. (2019). A Modular Deep Learning Approach for Extreme Multi-label Text Classification.. arXiv (Cornell University).7 indexed citations
7.
Chang, Wei-Cheng, Hsiang‐Fu Yu, Kai Zhong, Yiming Yang, & Inderjit S. Dhillon. (2019). X-BERT: eXtreme Multi-label Text Classification with BERT. arXiv (Cornell University).5 indexed citations
Chang, Wei-Cheng, Hsiang‐Fu Yu, Kai Zhong, Yiming Yang, & Inderjit S. Dhillon. (2019). X-BERT: eXtreme Multi-label Text Classification with using Bidirectional Encoder Representations from Transformers.14 indexed citations
Yen, Ian En-Hsu, et al.. (2018). MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. Neural Information Processing Systems. 31. 10868–10876.1 indexed citations
12.
Zhong, Kai, et al.. (2017). Fast Classification with Binary Prototypes. International Conference on Artificial Intelligence and Statistics. 1255–1263.9 indexed citations
13.
Zhong, Kai, Prateek Jain, & Inderjit S. Dhillon. (2016). Mixed Linear Regression with Multiple Components. Neural Information Processing Systems. 29. 2190–2198.7 indexed citations
14.
Lei, Qi, Kai Zhong, & Inderjit S. Dhillon. (2016). Coordinate-wise power method. neural information processing systems. 29. 2064–2072.15 indexed citations
15.
Yen, Ian En-Hsu, Xiangru Huang, Kai Zhong, Pradeep Ravikumar, & Inderjit S. Dhillon. (2016). PD-sparse: a primal and dual sparse approach to extreme multiclass and multilabel classification. International Conference on Machine Learning. 3069–3077.65 indexed citations
16.
Zhong, Kai, et al.. (2015). A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. International Conference on Machine Learning. 2418–2426.3 indexed citations
Zhong, Kai. (2004). Geophysical evidences of two*$-segment tectonic evolution of two*$-phase foreland basin in the western Sichuan province. Acta Petrologica Sinica.1 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.