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.
This map shows the geographic impact of Zhiting Hu'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 Zhiting Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhiting Hu more than expected).
This network shows the impact of papers produced by Zhiting Hu. 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 Zhiting Hu. The network helps show where Zhiting Hu may publish in the future.
Co-authorship network of co-authors of Zhiting Hu
This figure shows the co-authorship network connecting the top 25 collaborators of Zhiting Hu.
A scholar is included among the top collaborators of Zhiting Hu 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 Zhiting Hu. Zhiting Hu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tan, Bowen, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, & Eric P. Xing. (2019). Connecting the Dots Between MLE and RL for Sequence Generation. arXiv (Cornell University).5 indexed citations
Liang, Xiaodan, et al.. (2018). Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation. arXiv (Cornell University). 31. 1530–1540.54 indexed citations
14.
Liang, Xiaodan, Zhiting Hu, Hao Zhang, Liang Lin, & Eric P. Xing. (2018). Symbolic Graph Reasoning Meets Convolutions. Neural Information Processing Systems. 31. 1853–1863.73 indexed citations
15.
Xu, Haowen, et al.. (2018). AutoLoss: Learning Discrete Schedules for Alternate Optimization.. arXiv (Cornell University).4 indexed citations
16.
Hu, Zhiting, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, & Eric P. Xing. (2017). Controllable Text Generation.. arXiv (Cornell University).43 indexed citations
17.
Hu, Zhiting, Zichao Yang, Ruslan Salakhutdinov, & Eric P. Xing. (2017). On Unifying Deep Generative Models. International Conference on Learning Representations.11 indexed citations
Zheng, Ronghuo, et al.. (2016). Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification. International Conference on Computational Linguistics. 2678–2688.23 indexed citations
20.
Hu, Zhiting, Qirong Ho, Avinava Dubey, & Eric P. Xing. (2015). Large-scale Distributed Dependent Nonparametric Trees. International Conference on Machine Learning. 1651–1659.3 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.