Apr 9, 2019 · Abstract page for arXiv paper 1904.04789: Approximation in $L^p(μ)$ with deep ReLU neural networks.
scholar.google.com › citations
The expressive power of neural networks which use the non-smooth ReLU activation function ϱ(x) = max{0, x} by analyzing the approximation theoretic ...
These results fall into two categories: The first considers approximation in Lp using ReLU networks of fixed depth, while the second considers uniform ...
Namely, in function approximation, under certain conditions, single-hidden-layer neural networks which called shallow neural networks can approximate well ...
Missing: Lp( | Show results with:Lp(
Approximation in $L^p(\mu)$ with deep ReLU neural networks
www.researchgate.net › publication › 33...
Abstract. We discuss the expressive power of neural networks which use the non-smooth ReLU activation function ϱ ...
Missing: Lp( | Show results with:Lp(
Apr 9, 2019 · It is shown that the results concerning networks with fixed depth can be generalized to approximation in L^p(\mu)$, for any finite Borel ...
In this work, we solve this problem for the class of deep ReLU neural networks (Nair and Hinton, 2010) when approximating functions lying in a Sobolev or Besov ...
We investigate non-adaptive methods of deep ReLU neural network approximation in Bochner spaces L 2 ( U ∞ , X , μ ) of functions on U ∞ taking values in a ...
Missing: Lp( | Show results with:Lp(
This article is concerned with the approximation and expressive powers of deep neural net- works. This is an active research area currently producing many ...
People also ask
What are the advantages of using leaky rectified linear units leaky ReLU over normal ReLU in deep learning?
Why is ReLU used in neural networks?
Jan 25, 2021 · We propose an efficient, deterministic algorithm for constructing exponentially con- vergent deep neural network (DNN) approximations of ...
Missing: Lp( | Show results with:Lp(