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.
Learning to rank
20071.2k citationsTao Qin, Tie‐Yan Liu et al.profile →
BioGPT: generative pre-trained transformer for biomedical text generation and mining
2022510 citationsRenqian Luo, Yingce Xia et al.Briefings in Bioinformaticsprofile →
Image-to-Image Translation: Methods and Applications
This map shows the geographic impact of Tao Qin'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 Tao Qin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tao Qin more than expected).
This network shows the impact of papers produced by Tao Qin. 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 Tao Qin. The network helps show where Tao Qin may publish in the future.
Co-authorship network of co-authors of Tao Qin
This figure shows the co-authorship network connecting the top 25 collaborators of Tao Qin.
A scholar is included among the top collaborators of Tao Qin 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 Tao Qin. Tao Qin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ren, Yi, Xu Tan, Tao Qin, Zhou Zhao, & Tie‐Yan Liu. (2022). Revisiting Over-Smoothness in Text to Speech. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 8197–8213.25 indexed citations
Chen, Jiawei, Xu Tan, Yichong Leng, et al.. (2021). Speech-T: Transducer for Text to Speech and Beyond. Neural Information Processing Systems. 34.4 indexed citations
Xia, Yingce, Xu Tan, Fei Tian, et al.. (2018). Model-Level Dual Learning. International Conference on Machine Learning. 5383–5392.22 indexed citations
16.
Gong, Chengyue, Di He, Xu Tan, et al.. (2018). FRAGE: Frequency-Agnostic Word Representation. Neural Information Processing Systems. 31. 1334–1345.26 indexed citations
17.
Han, The Anh, Yingce Xia, Tao Qin, & Nicholas R. Jennings. (2015). Efficient Algorithms with performance guarantees for the stochastic multiple-choice knapsack problem. ePrints Soton (University of Southampton).1 indexed citations
18.
Xia, Yingce, Wenkui Ding, Xudong Zhang, Nenghai Yu, & Tao Qin. (2015). Budgeted Bandit Problems with Continuous Random Costs. Asian Conference on Machine Learning. 317–332.9 indexed citations
Qin, Tao, Xiubo Geng, & Tie‐Yan Liu. (2010). A New Probabilistic Model for Rank Aggregation. Neural Information Processing Systems. 23. 1948–1956.52 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.