A global universality of two-layer neural networks with ReLU activations
This work addresses the theoretical understanding of neural network capabilities for researchers in machine learning theory, providing a global convergence guarantee.
This paper investigates the universality of two-layer neural networks with ReLU activations, focusing on their density in function spaces. It establishes global convergence by introducing a suitable norm, ensuring uniformity over any compact set.
In the present study, we investigate a universality of neural networks, which concerns a density of the set of two-layer neural networks in a function spaces. There are many works that handle the convergence over compact sets. In the present paper, we consider a global convergence by introducing a norm suitably, so that our results will be uniform over any compact set.