scholar.google.com › citations
Nov 25, 2022 · Abstract:We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical ...
Feb 1, 2023 · We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical systems ...
Nov 18, 2022 · However, limited research has explored the universal approximation properties of fully convolutional neural networks as arbitrary continuous ...
People also ask
What is the universal approximation theorem for convolutional neural networks?
What is the approximation theorem in deep learning?
What is the approximation theory of neural networks?
What are the properties of Convolutional Neural Network?
We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical systems perspective. We prove.
We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical systems perspective.
It is proved that deep residual fully convolutional networks and their continuous-layer counterpart can achieve universal approximation of shift-invariant ...
On the Universal Approximation Property of Deep Fully Convolutional Neural Networks ... deep residual fully convolutional networks and their continuous-layer ...
Nov 28, 2022 · We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical systems ...
... approximate a target function by deep and wide ReLU neural networks. ... fully connected ReLU network F {\displaystyle F}. {\displaystyle F}. of width ...
In this paper, we proved that under suitable conditions, convolution neural networks can inherit the universal approximation property of its last fully ...