1 citations · 1 across the 3 of their papers we have counts for
4 papers
Deep learning and the rate of approximation by flows
Jingpu Cheng, Qianxiao Li, Ting Lin +1
We investigate the dependence of the approximation capacity of deep residual networks on its depth in a continuous dynamical systems setting. This can be formulated as the general…
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…