5 citations · 9 across the 7 of their papers we have counts for
6 papers · 1 filter
Layer-wise Geometric Approximation Rates for Deep Networks
Shijun Zhang, Zuowei Shen, Yuesheng Xu
Depth is widely viewed as a central contributor to the success of deep neural networks, whereas standard neural network approximation theory typically provides guarantees only for…
Multigrade Neural Network Approximation
Shijun Zhang, Zuowei Shen, Yuesheng Xu
We study multigrade deep learning (MGDL) as a principled framework for structured error refinement in deep neural networks. While the approximation power of neural networks is now…
On the Universal Approximation Property of Deep Fully Convolutional Neural Networks
Ting Lin, Zuowei Shen, Qianxiao Li
We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical systems perspective. We prove that deep residual full…
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems
Qianxiao Li, Ting Lin, Zuowei Shen
We study the approximation of functions which are invariant with respect to certain permutations of the input indices using flow maps of dynamical systems. Such invariant functions…
Neural Network Architecture Beyond Width and Depth
Zuowei Shen, Haizhao Yang, Shijun Zhang
This paper proposes a new neural network architecture by introducing an additional dimension called height beyond width and depth. Neural network architectures with height, width,…
IAE-Net: Integral Autoencoders for Discretization-Invariant Learning
Yong Zheng Ong, Zuowei Shen, Haizhao Yang
Discretization invariant learning aims at learning in the infinite-dimensional function spaces with the capacity to process heterogeneous discrete representations of functions as i…