让下一篇重要论文，不再错过。

On shallow feedforward neural networks with inputs from a topological space

Vugar Ismailov

We study feedforward neural networks with inputs from a topological space (TFNNs). We prove a universal approximation theorem for shallow TFNNs, which demonstrates their capacity to approximate any continuous function defined on this topological space. As an application, we obtain an approximative version of Kolmogorov's superposition theorem for compact metric spaces.

尚无 AI 解读，展示原文摘要