Dec 22, 2019 · We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...

Abstract. We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...

We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, ...

In this paper, we establish some basic results on the approximation mechanism of composition, building on the dynamical systems approach to deep learning, where ...

In this paper, we establish some basic results on the approximation capabilities of deep learning models in the form of dynamical systems. In particular, we ...

A brief introduction. For a given function f : Rd → R and ε > 0, approximation is to find a simple function g such that f − g < ε.

Dec 22, 2019 · This work has shown that by idealizing deep residual networks (ResNet) as continuous-time dynamical systems it is possible to derive ...

Bibliographic details on Deep Learning via Dynamical Systems: An Approximation Perspective.

101, 2022. Deep learning via dynamical systems: An approximation perspective. Q Li, T Lin, Z Shen. Journal of the European Mathematical Society 25 (5), 1671 ...

Dec 22, 2019 · In this paper, we establish some basic results on the approximation capabilities of deep learning models in the form of dynamical systems. In ...