2 citations · 8 across the 6 of their papers we have counts for
10 papers
Universal Consistency of Deep Convolutional Neural Networks
Shao-Bo Lin, Kaidong Wang, Yao Wang +1
Compared with avid research activities of deep convolutional neural networks (DCNNs) in practice, the study of theoretical behaviors of DCNNs lags heavily behind. In particular, th…
Kernel-based L_2-Boosting with Structure Constraints
Yao Wang, Xin Guo, Shao-Bo Lin
Developing efficient kernel methods for regression is very popular in the past decade. In this paper, utilizing boosting on kernel-based weaker learners, we propose a novel kernel-…
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization
Zhi Han, Siquan Yu, Shao-Bo Lin +1
Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques…
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Jinshan Zeng, Min Zhang, Shao-Bo Lin
Boosting is a well-known method for improving the accuracy of weak learners in machine learning. However, its theoretical generalization guarantee is missing in literature. In this…
Distributed Kernel Ridge Regression with Communications
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
This paper focuses on generalization performance analysis for distributed algorithms in the framework of learning theory. Taking distributed kernel ridge regression (DKRR) for exam…
Realization of spatial sparseness by deep ReLU nets with massive data
Charles K. Chui, Shao-Bo Lin, Bo Zhang +1
The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognize…