DNNs approximate sequences of functions constructed via finite compositions of locally Lipschitz continuous functions, maxima, and products with polynomial parameter growth in d and 1/ε.
On the approximate realization of continuous mappings by neural networks
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Deep neural network approximation theory for high-dimensional functions
DNNs approximate sequences of functions constructed via finite compositions of locally Lipschitz continuous functions, maxima, and products with polynomial parameter growth in d and 1/ε.