| Distributed learning with regularized least squares SB Lin, X Guo, DX Zhou The Journal of Machine Learning Research 18 (1), 3202-3232, 2017 | 173 | 2017 |
| Is extreme learning machine feasible? A theoretical assessment (Part I) X Liu, S Lin, J Fang, Z Xu IEEE Transactions on Neural Networks and Learning Systems 26 (1), 7-20, 2014 | 155 | 2014 |
| Regularization: Convergence of Iterative Half Thresholding Algorithm J Zeng, S Lin, Y Wang, Z Xu IEEE Transactions on Signal Processing 62 (9), 2317-2329, 2014 | 150 | 2014 |
| Is extreme learning machine feasible? A theoretical assessment (Part II) S Lin, X Liu, J Fang, Z Xu IEEE Transactions on Neural Networks and Learning Systems 26 (1), 21-34, 2014 | 132 | 2014 |
| Learning theory of distributed spectral algorithms ZC Guo, SB Lin, DX Zhou Inverse Problems 33 (7), 074009, 2017 | 115 | 2017 |
| Distributed semi-supervised learning with kernel ridge regression X Chang, SB Lin, DX Zhou The Journal of Machine Learning Research 18 (1), 1493-1514, 2017 | 98 | 2017 |
| Global convergence of block coordinate descent in deep learning J Zeng, TTK Lau, SB Lin, Y Yao ICML, 2019 | 86* | 2019 |
| Distributed kernel-based gradient descent algorithms SB Lin, DX Zhou Constructive Approximation 47 (2), 249-276, 2018 | 64 | 2018 |
| Sparse regularization: Convergence of iterative jumping thresholding algorithm J Zeng, S Lin, Z Xu IEEE Transactions on Signal Processing 64 (19), 5106-5118, 2016 | 44 | 2016 |
| Divide and conquer local average regression X Chang, SB Lin, Y Wang | 39 | 2017 |
| Generalization and Expressivity for Deep Nets SB Lin IEEE Transactions on Neural Networks and Learning Systems, 2018 | 32 | 2018 |
| Deep Neural Networks for Rotation-Invariance Approximation and Learning CK Chui, SB Lin, DX Zhou Analysis and Applications 17 (5), 737-772, 2019 | 28 | 2019 |
| Construction of neural networks for realization of localized deep learning CK Chui, SB Lin, DX Zhou Frontiers in Applied Mathematics and Statistics, 14, 2018 | 27 | 2018 |
| Distributed learning with multi-penalty regularization ZC Guo, SB Lin, L Shi Applied and Computational Harmonic Analysis 46 (3), 478-499, 2019 | 26 | 2019 |
| Approximation capability of interpolation neural networks F Cao, S Lin, Z Xu Neurocomputing 74 (1-3), 457-460, 2010 | 26 | 2010 |
| Almost optimal estimates for approximation and learning by radial basis function networks S Lin, X Liu, Y Rong, Z Xu Machine learning 95, 147-164, 2014 | 24 | 2014 |
| Distributed Kernel Ridge Regression with Communications. SB Lin, D Wang, DX Zhou J. Mach. Learn. Res. 21, 93:1-93:38, 2020 | 23* | 2020 |
| Constructive neural network learning S Lin, J Zeng, X Zhang IEEE transactions on cybernetics 49 (1), 221-232, 2018 | 23 | 2018 |
| Learning Capability of Relaxed Greedy Algorithms S Lin, Y Rong, X Sun, Z Xu IEEE Transaction on Neural Networks and Learning Systems 24 (10), 1598-1608, 2013 | 21 | 2013 |
| Realization of spatial sparseness by deep ReLU nets with massive data CK Chui, SB Lin, B Zhang, DX Zhou IEEE Transactions on Neural Networks and Learning Systems 33 (1), 229-243, 2020 | 20 | 2020 |
Ding-Xuan ZhouUniversity of Sydney在 sydney.edu.au 的电子邮件经过验证
