Cheng Tai

1.2k total citations
11 papers, 355 citations indexed

About

Cheng Tai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Cheng Tai has authored 11 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Computational Mechanics. Recurrent topics in Cheng Tai's work include Image and Signal Denoising Methods (2 papers), Mathematical Biology Tumor Growth (2 papers) and Gaussian Processes and Bayesian Inference (1 paper). Cheng Tai is often cited by papers focused on Image and Signal Denoising Methods (2 papers), Mathematical Biology Tumor Growth (2 papers) and Gaussian Processes and Bayesian Inference (1 paper). Cheng Tai collaborates with scholars based in China, United States and Singapore. Cheng Tai's co-authors include E Weinan, Xiaogang Wang, Tong Xiao, Yi Zhang, Liping Wei, Meng Wang, Qianxiao Li, Long Chen, Zuowei Shen and Xiaoqun Zhang and has published in prestigious journals such as Nucleic Acids Research, Journal of Machine Learning Research and SIAM Journal on Imaging Sciences.

In The Last Decade

Cheng Tai

11 papers receiving 332 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Cheng Tai China	6	194	128	71	40	30	11	355
Giorgos Bouritsas United Kingdom	7	155 0.8×	155 1.2×	22 0.3×	60 1.5×	9 0.3×	9	322
Chao Lan United States	11	148 0.8×	151 1.2×	16 0.2×	24 0.6×	8 0.3×	43	325
Guillaume Alain Canada	7	215 1.1×	186 1.5×	21 0.3×	17 0.4×	17 0.6×	8	410
Tingting Zhao China	12	274 1.4×	116 0.9×	37 0.5×	11 0.3×	20 0.7×	31	439
Jinfeng Pan China	12	128 0.7×	67 0.5×	146 2.1×	10 0.3×	27 0.9×	41	372
Kyoungsu Oh South Korea	6	129 0.7×	140 1.1×	19 0.3×	30 0.8×	50 1.7×	33	356
Zhenqiu Shu China	14	408 2.1×	138 1.1×	25 0.4×	46 1.1×	18 0.6×	69	587
Kaidi Cao United States	10	364 1.9×	184 1.4×	44 0.6×	42 1.1×	19 0.6×	18	519
Zhihui Zhang China	12	83 0.4×	123 1.0×	24 0.3×	6 0.1×	39 1.3×	44	436

Countries citing papers authored by Cheng Tai

Since Specialization

Citations

This map shows the geographic impact of Cheng Tai'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 Cheng Tai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cheng Tai more than expected).

Fields of papers citing papers by Cheng Tai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cheng Tai. 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 Cheng Tai. The network helps show where Cheng Tai may publish in the future.

Co-authorship network of co-authors of Cheng Tai

This figure shows the co-authorship network connecting the top 25 collaborators of Cheng Tai. A scholar is included among the top collaborators of Cheng Tai 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 Cheng Tai. Cheng Tai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Liu, Yuxuan, et al.. (2024). Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering. 4372–4382. 5 indexed citations

2.

Bao, Chenglong, Qianxiao Li, Zuowei Shen, et al.. (2023). Approximation Analysis of Convolutional Neural Networks. East Asian Journal on Applied Mathematics. 13(3). 524–549. 5 indexed citations

3.

Li, Qianxiao, Cheng Tai, & E Weinan. (2019). Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. 20(40). 1–47. 5 indexed citations

4.

Wang, Meng, Cheng Tai, E Weinan, & Liping Wei. (2018). DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants. Nucleic Acids Research. 46(11). e69–e69. 78 indexed citations

5.

Wu, Lei, Zhanxing Zhu, Cheng Tai, & E Weinan. (2018). Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient. 1 indexed citations

6.

Li, Qianxiao, Long Chen, Cheng Tai, & E Weinan. (2018). Maximum Principle Based Algorithms for Deep Learning. 18(165). 1–29. 49 indexed citations

7.

Tai, Cheng, Tong Xiao, Yi Zhang, Xiaogang Wang, & E Weinan. (2016). Convolutional neural networks with low-rank regularization. arXiv (Cornell University). 171 indexed citations

8.

Tai, Cheng & E Weinan. (2015). Multiscale Adaptive Representation of Signals: I. The Basic Framework. Journal of Machine Learning Research. 17(1). 4875–4912. 10 indexed citations

9.

Tai, Cheng, Xiaoqun Zhang, & Zuowei Shen. (2013). Wavelet Frame Based Multiphase Image Segmentation. SIAM Journal on Imaging Sciences. 6(4). 2521–2546. 21 indexed citations

10.

Tai, Cheng, et al.. (2005). A Finite Volume-Based High Order Cartesian Cut-Cell Method for Computational Aeroacoustics. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5 indexed citations

11.

Zheng, Chichao, et al.. (1995). DETECTION OF ECG CHARAC TERISTIC POINT USING WAVELET TRANSFORM. 42. 21–28. 5 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.

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