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 Tie‐Yan Liu'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 Tie‐Yan Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tie‐Yan Liu more than expected).
This network shows the impact of papers produced by Tie‐Yan Liu. 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 Tie‐Yan Liu. The network helps show where Tie‐Yan Liu may publish in the future.
Co-authorship network of co-authors of Tie‐Yan Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Tie‐Yan Liu.
A scholar is included among the top collaborators of Tie‐Yan Liu 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 Tie‐Yan Liu. Tie‐Yan Liu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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
4.
Ke, Guolin, Di He, & Tie‐Yan Liu. (2021). Rethinking Positional Encoding in Language Pre-training. International Conference on Learning Representations.82 indexed citations
5.
Zhang, He, et al.. (2021). Co-evolution Transformer for Protein Contact Prediction. Neural Information Processing Systems. 34.5 indexed citations
6.
Song, Kaitao, Xu Tan, Tao Qin, Jianfeng Lu, & Tie‐Yan Liu. (2020). MPNet: Masked and Permuted Pre-training for Language Understanding. arXiv (Cornell University). 33. 16857–16867.18 indexed citations
7.
He, Di, et al.. (2019). Efficient Training of BERT by Progressively Stacking. International Conference on Machine Learning. 2337–2346.39 indexed citations
8.
Meng, Qi, Shuxin Zheng, Huishuai Zhang, et al.. (2018). G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space.. International Conference on Learning Representations.6 indexed citations
9.
Zhang, Huishuai, Wei Chen, & Tie‐Yan Liu. (2018). On the Local Hessian in Back-propagation. Neural Information Processing Systems. 31. 6520–6530.3 indexed citations
10.
Xia, Yingce, Xu Tan, Fei Tian, et al.. (2018). Model-Level Dual Learning. International Conference on Machine Learning. 5383–5392.22 indexed citations
11.
Gong, Chengyue, Di He, Xu Tan, et al.. (2018). FRAGE: Frequency-Agnostic Word Representation. Neural Information Processing Systems. 31. 1334–1345.26 indexed citations
12.
Li, Zhuohan, Di He, Fei Tian, et al.. (2018). Towards Binary-Valued Gates for Robust LSTM Training. International Conference on Machine Learning. 2995–3004.2 indexed citations
13.
Liu, Tie‐Yan, et al.. (2018). Boosting Dynamic Programming with Neural Networks for Solving NP-hard Problems. Asian Conference on Machine Learning. 726–739.9 indexed citations
14.
He, Tianyu, Xu Tan, Yingce Xia, et al.. (2018). Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation. Neural Information Processing Systems. 31. 7944–7954.52 indexed citations
15.
Wu, Lijun, Yingce Xia, Fei Tian, et al.. (2018). Adversarial Neural Machine Translation. Asian Conference on Machine Learning. 534–549.35 indexed citations
16.
Chen, Wei, et al.. (2017). Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting. arXiv (Cornell University). 30. 5504–5513.8 indexed citations
17.
Zheng, Shuxin, Qi Meng, Taifeng Wang, et al.. (2016). Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning.. arXiv (Cornell University).11 indexed citations
18.
Cui, Qing, et al.. (2014). Co-learning of Word Representations and Morpheme Representations. International Conference on Computational Linguistics. 141–150.44 indexed citations
19.
Tian, Fei, Hanjun Dai, Jiang Bian, et al.. (2014). A Probabilistic Model for Learning Multi-Prototype Word Embeddings. International Conference on Computational Linguistics. 151–160.68 indexed citations
20.
Wang, Yining, Liwei Wang, Yuanzhi Li, Di He, & Tie‐Yan Liu. (2013). A Theoretical Analysis of NDCG Type Ranking Measures. Conference on Learning Theory. 25–54.61 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.