2 papers
cs.LG2026
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…
cs.LG2025
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…