Polynomial Approximation

Polynomial approximation focuses on representing complex functions using polynomials, aiming for efficient computation and improved model interpretability. Current research emphasizes developing novel algorithms for constructing accurate polynomial approximations, particularly within neural networks (e.g., Polynomial-Augmented Neural Networks) and for specific applications like solving partial differential equations and enabling privacy-preserving machine learning. These advancements are significant for various fields, improving the efficiency and accuracy of machine learning models, enhancing the analysis of complex systems, and enabling secure computations in sensitive applications.