78 citations · 412 across the 48 of their papers we have counts for
6 papers · 1 filter
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…
Towards Understanding the Spectral Bias of Deep Learning
Yuan Cao, Zhiying Fang, Yue Wu +2
An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions. The pr…
Fast Polynomial Kernel Classification for Massive Data
Jinshan Zeng, Minrun Wu, Shao-Bo Lin +1
In the era of big data, it is desired to develop efficient machine learning algorithms to tackle massive data challenges such as storage bottleneck, algorithmic scalability, and in…
Distributed filtered hyperinterpolation for noisy data on the sphere
Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou
Problems in astrophysics, space weather research and geophysics usually need to analyze noisy big data on the sphere. This paper develops distributed filtered hyperinterpolation fo…
Deep Neural Networks for Rotation-Invariance Approximation and Learning
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions…
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
Jinshan Zeng, Shao-Bo Lin, Yuan Yao +1
In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \textit{sigmoid-ADM…