9 citations · 12 across the 5 of their papers we have counts for
6 papers
A unified framework for establishing the universal approximation of transformer-type architectures
Jingpu Cheng, Ting Lin, Zuowei Shen +1
We investigate the universal approximation property (UAP) of transformer-type architectures, providing a unified theoretical framework that extends prior results on residual networ…
Analysis of a wavelet frame based two-scale model for enhanced edges
Bin Dong, Ting Lin, Zuowei Shen +1
Image restoration is a class of important tasks that emerges from a wide range of scientific disciplines. It has been noticed that most practical images can be modeled as a composi…
Interpolation, Approximation and Controllability of Deep Neural Networks
Jingpu Cheng, Qianxiao Li, Ting Lin +1
We investigate the expressive power of deep residual neural networks idealized as continuous dynamical systems through control theory. Specifically, we consider two properties that…
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…
Distributed Training Large-Scale Deep Architectures
Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu +6
Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorith…