From a statistical learning perspective, modern neural networks can indeed be understood as a large-scale maximum likelihood estimation (MLE) process. Specifically, a neural network is a parameterized function, and the most common way to train a neural network is to perform maximum likelihood estimation on the data.

The core purpose of UAT (Universal Approximation Theorem) is that neural networks can approximate any continuous function, and continuous functions can be approximated by polynomials (Weierstrass theorem).

The Fréchet Mean Prototype is the center point in a metric space that minimizes the sum of the squared distances to all data points. It extends the averaging operation from flat vector spaces to arbitrary curved spaces or complex objects.

The curse of dimensionality is not a specific geometric structure, but a collection of phenomena induced by the intrinsic properties of high-dimensional spaces.

The von Mises–Fisher distribution (vMF) distribution is often regarded as the hyperspherical analogue of the Normal distribution, as it models data concentrated around a mean direction on the unit hypersphere.