Functional Equivalence and Geometric Diversity in Neural Network Approximations: An Empirical Characterization

The Universal Approximation Theorem states that a neural network with a single hidden layer is sufficient to approximate any continuous univariate function on a compact domain to arbitrary error. However, the uniqueness of such neural network representations is not guaranteed, raising questions about practical identifiability.

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