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NP-Hardness of Minimizing Neurons in Two-Hidden-Layer ReLU Neural Networks
A fundamental question in neural network architecture optimization is whether the minimum hidden-neuron count required to approximate a target function within a prescribed tolerance can be computed efficiently. This paper resolves this question for two-hidden-layer ReLU networks under an $L^p(\mathb
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