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.
Parameter-efficient fine-tuning of large-scale pre-trained language models
2023420 citationsNing Ding, Yujia Qin et al.Nature Machine Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jianfei Chen'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 Jianfei Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jianfei Chen more than expected).
This network shows the impact of papers produced by Jianfei Chen. 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 Jianfei Chen. The network helps show where Jianfei Chen may publish in the future.
Co-authorship network of co-authors of Jianfei Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Jianfei Chen.
A scholar is included among the top collaborators of Jianfei Chen 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 Jianfei Chen. Jianfei Chen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chen, Jianfei, Lianmin Zheng, Zhewei Yao, et al.. (2021). ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training. International Conference on Machine Learning. 1803–1813.3 indexed citations
12.
Chen, Jianfei, et al.. (2020). VFlow: More Expressive Generative Flows with Variational Data Augmentation. International Conference on Machine Learning. 1. 1660–1669.2 indexed citations
13.
Chen, Jianfei, Jun Zhu, & Le Song. (2018). Stochastic Training of Graph Convolutional Networks with Variance Reduction. International Conference on Machine Learning. 941–949.57 indexed citations
14.
Chen, Jianfei, Jun Zhu, Yee Whye Teh, & Tong Zhang. (2018). Stochastic Expectation Maximization with Variance Reduction. Oxford University Research Archive (ORA) (University of Oxford). 31. 7967–7977.12 indexed citations
15.
Chen, Jianfei, et al.. (2017). Population Matching Discrepancy and Applications in Deep Learning. Neural Information Processing Systems. 30. 6262–6272.4 indexed citations
16.
Chen, Jianfei, Kaiwei Li, Jun Zhu, & Wenguang Chen. (2015). WarpLDA: a Simple and Efficient O(1) Algorithm for Latent Dirichlet Allocation.. arXiv (Cornell University).3 indexed citations
Li, Chengtao, Jun Zhu, & Jianfei Chen. (2014). Bayesian Max-margin Multi-Task Learning with Data Augmentation. International Conference on Machine Learning. 415–423.11 indexed citations
19.
Chen, Jianfei, Jun Zhu, Zi Wang, Xun Zheng, & Bo Zhang. (2013). Scalable Inference for Logistic-Normal Topic Models. Neural Information Processing Systems. 26. 2445–2453.35 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.