arXiv:1906.11945math.NA
Error bounds for deep ReLU networks using the Kolmogorov--Arnold superposition theorem
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Abstract
We prove a theorem concerning the approximation of multivariate functions by deep ReLU networks, for which the curse of the dimensionality is lessened. Our theorem is based on a constructive proof of the Kolmogorov--Arnold superposition theorem, and on a subset of multivariate continuous functions whose outer superposition functions can be efficiently approximated by deep ReLU networks.
If you like this, you may also like this 2019 research paper: "Deep networks and the Kolmogorov–Arnold theorem" https://hadrien-montanelli.github.io/2019-06-25.html https://arxiv.org/pdf/1906.11945
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