EML Trees are Universal Approximators [R]
Summary
This paper proves that EML trees, which represent elementary functions through composition, are universal approximators for continuous functions and other functional spaces. The proof constructs EML representations of basic operations and uses them as building blocks.
Similar Articles
Generalized Neurons
The article explores the Universal Approximation Theorem in deep learning, analyzing the representation capacity of individual neurons and neural network layers using ReLU activation functions.
Universal Approximation of Nonlinear Operators and Their Derivatives
This paper proves the first universal approximation theorems for nonlinear operators and their derivatives in infinite-dimensional settings, extending classical results to operator learning architectures like DeepONet and PCA-Net.
On Explicit Super-Expressive Approximation for Neural Networks
This paper investigates fixed-architecture neural network approximation with explicit parameter-error trade-offs, using the Chinese Remainder Theorem as a constructive encoding mechanism, and achieves explicit bounds for Lipschitz and Hölder-smooth functions.
Universality and Approximation Rates of Graph Neural Networks with Random Features
This paper proves that graph neural networks with random node features can universally approximate permutation-invariant or equivariant functions on directed graphs, and provides approximation rate bounds for differentiable functions.
Lifting E-Graphs
The article presents 'Lifting E-Graphs', a refined approach to e-graphs that explicitly encodes the context (dimension) of functions to resolve issues with variable naming, missed sharing, and accidental over-sharing, based on a semantic model of functions from R^n to R.