Jul 9, 2020 · Abstract:In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks.
In this chapter, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical ...
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We propose a new approach to the problem of neural network expressivity, which seeks to char- acterize how structural properties of a neural net-.
To describe the expressive power of neural networks, we begin by showing that without any restriction on the architecture Neural networks are fully expressive.
We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family ...
Jun 16, 2016 · We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family ...
Apr 21, 2020 · Our theoretical analysis reveals that DNNs use spatial domains for information representation and evolve to the edge of chaos as depth increases.
Our results reveal a phase transition in the expressivity of random deep networks, with networks in the chaotic phase computing nonlinear functions whose global ...
May 30, 2020 · Expressivity is often used to talk about what classes of functions a neural network can approximate / learn, while capacity measure some notion of how much ...
The first is that deep networks can compactly express highly complex functions over input space in a way that shallow networks with one hidden layer and the ...