An elementary proof of a universal approximation theorem
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Abstract (arXiv)
In this short note, we give an elementary proof of a universal approximation theorem for neural networks with three hidden layers and increasing, continuous, bounded activation function. The result is weaker than the best known results, but the proof is elementary in the sense that no machinery beyond undergraduate analysis is used.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 0 of its 3 references that are in the map. First posted on arXiv 14 Jun 2024.
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