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Found 5 papers, 0 papers with code
Expressivity and Approximation Properties of Deep Neural Networks with ReLU$^k$ Activation
Additionally, through an exploration of the representation power of deep ReLU$^k$ networks for shallow networks, we reveal that deep ReLU$^k$ networks can approximate functions from a range of variation spaces, extending beyond those generated solely by the ReLU$^k$ activation function.
Tractability of approximation by general shallow networks
In this paper, we present a sharper version of the results in the paper Dimension independent bounds for general shallow networks; Neural Networks, \textbf{123} (2020), 142-152.
Rates of Approximation by ReLU Shallow Neural Networks
We show that ReLU shallow neural networks with $m$ hidden neurons can uniformly approximate functions from the H\"older space $W_\infty^r([-1, 1]^d)$ with rates $O((\log m)^{\frac{1}{2} +d}m^{-\frac{r}{d}\frac{d+2}{d+4}})$ when $r<d/2 +2$.
Encoding of data sets and algorithms
This involves creating a grid on the hypothetical spaces of data sets and algorithms so as to identify a finite set of probability distributions from which the data sets are sampled and a finite set of algorithms.
Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions
We consider a family of deep neural networks consisting of two groups of convolutional layers, a downsampling operator, and a fully connected layer.
