| Convolutional neural networks with low-rank regularization C Tai, T Xiao, Y Zhang, X Wang arXiv preprint arXiv:1511.06067, 2015 | 566 | 2015 |
| Stochastic modified equations and adaptive stochastic gradient algorithms Q Li, C Tai, E Weinan International Conference on Machine Learning, 2101-2110, 2017 | 333 | 2017 |
| Maximum principle based algorithms for deep learning Q Li, L Chen, C Tai, E Weinan Journal of Machine Learning Research 18 (165), 1-29, 2018 | 253 | 2018 |
| Stochastic modified equations and dynamics of stochastic gradient algorithms i: Mathematical foundations Q Li, C Tai, E Weinan Journal of Machine Learning Research 20 (40), 1-47, 2019 | 191 | 2019 |
| Understanding and enhancing the transferability of adversarial examples L Wu, Z Zhu, C Tai arXiv preprint arXiv:1802.09707, 2018 | 132 | 2018 |
| DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants M Wang, C Tai, W E, L Wei Nucleic acids research 46 (11), e69-e69, 2018 | 108 | 2018 |
| Wavelet frame based multiphase image segmentation C Tai, X Zhang, Z Shen SIAM Journal on Imaging Sciences 6 (4), 2521-2546, 2013 | 36 | 2013 |
| Multiscale adaptive representation of signals: I. the basic framework C Tai, E Weinan Journal of Machine Learning Research 17 (140), 1-38, 2016 | 22 | 2016 |
| Dynamics of stochastic gradient algorithms Q Li, C Tai, E Weinan CoRR, 2015 | 21 | 2015 |
| Approximation analysis of convolutional neural networks C Bao, Q Li, Z Shen, C Tai, L Wu, X Xiang work 65, 871, 2014 | 20 | 2014 |
| Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering H Sun, Y Liu, C Wu, H Yan, C Tai, X Gao, S Shang, R Yan Proceedings of the ACM on Web Conference 2024, 4372-4382, 2024 | 6 | 2024 |
Qianxiao LiAssistant Professor, Department of Mathematics and Institute for Functional Intelligent MaterialsZweryfikowany adres z nus.edu.sg
