Shao-Bo Lin

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Ding-Xuan ZhouUniversity of SydneyEmail yang diverifikasi di sydney.edu.au
Jinshan ZengSchool of Computer and Information Engineering, Jiangxi Normal UniversityEmail yang diverifikasi di ust.hk
Yao WangXi'an Jiaotong UniversityEmail yang diverifikasi di xjtu.edu.cn
Jian FangNovartis Institutes for Biomedical ResearchEmail yang diverifikasi di novartis.com
Xiangyu ChangSchool of Management, Xi'an Jiaotong UniversityEmail yang diverifikasi di xjtu.edu.cn
Yuan YAOHong Kong University of Science and TechnologyEmail yang diverifikasi di ust.hk
Xin Guo 郭昕School of Mathematics and Physics, The University of QueenslandEmail yang diverifikasi di uq.edu.au
Di Wang西安交通大学Email yang diverifikasi di xjtu.edu.cn
Xingping SunProfessor of MathematicsEmail yang diverifikasi di missouristate.edu
Zhi HanSIA, CASEmail yang diverifikasi di sia.cn
Yunwen LeiThe University of Hong KongEmail yang diverifikasi di hku.hk
Shaojie TangUniversity at BuffaloEmail yang diverifikasi di buffalo.edu
Yu Guang WangAssociate Professor, Shanghai Jiao Tong University, University of New South WalesEmail yang diverifikasi di sjtu.edu.cn
Han FengCity University of Hong KongEmail yang diverifikasi di cityu.edu.hk
Charles K. ChuiDepartment of Statistics, Stanford University
Feilong CaoChina Jiliang University

Ikuti

Shao-Bo Lin

Xi'an Jiaotong University

Email yang diverifikasi di xjtu.edu.cn

Learning theory Approximation theory AI in Management Science


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Distributed learning with regularized least squares SB Lin, X Guo, DX Zhou Journal of Machine Learning Research 18 (92), 1-31, 2017	233	2017
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	188	2014
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	178	2014
Learning theory of distributed spectral algorithms ZC Guo, SB Lin, DX Zhou Inverse Problems 33 (7), 074009, 2017	149	2017
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	146	2014
Global convergence of block coordinate descent in deep learning J Zeng, TTK Lau, SB Lin, Y Yao ICML, 2019	133*	2019
Distributed semi-supervised learning with kernel ridge regression X Chang, SB Lin, DX Zhou Journal of Machine Learning Research 18 (46), 1-22, 2017	125	2017
Distributed kernel-based gradient descent algorithms SB Lin, DX Zhou Constructive Approximation 47 (2), 249-276, 2018	87	2018
Generalization and Expressivity for Deep Nets SB Lin IEEE Transactions on Neural Networks and Learning Systems, 2018	55	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	49	2016
Deep Neural Networks for Rotation-Invariance Approximation and Learning CK Chui, SB Lin, DX Zhou Analysis and Applications 17 (5), 737-772, 2019	48	2019
Depth selection for deep ReLU nets in feature extraction and generalization Z Han, S. Yu, SB Lin, DX Zhou IEEE Transactions on Pattern Analysis and Machine Intelligence 1 (1), 1-15, 2020	46*	2020
Divide and conquer local average regression X Chang, SB Lin, Y Wang	46	2017
Distributed Kernel Ridge Regression with Communications. SB Lin, D Wang, DX Zhou J. Mach. Learn. Res. 21, 93:1-93:38, 2020	43	2020
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	37	2020
Distributed learning with multi-penalty regularization ZC Guo, SB Lin, L Shi Applied and Computational Harmonic Analysis 46 (3), 478-499, 2019	34	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	34	2018
On ADMM in deep learning: Convergence and saturation-avoidance J Zeng, SB Lin, Y Yao, DX Zhou Journal of Machine Learning Research 22 (199), 1-67, 2021	33*	2021
Constructive neural network learning S Lin, J Zeng, X Zhang IEEE transactions on cybernetics 49 (1), 221-232, 2018	33	2018
Random sketching for neural networks with ReLU D Wang, J Zeng, SB Lin IEEE transactions on neural networks and learning systems 32 (2), 748-762, 2020	29	2020

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