Dimensional Polynomial

Dimensional polynomials are the focus of current research exploring how to efficiently learn and represent high-dimensional polynomial functions, particularly within machine learning contexts. Researchers are investigating neural network architectures, including those incorporating equivariance to specific groups like SL(2,ℝ), and algorithms like stochastic gradient descent, to optimize the learning of these polynomials, often focusing on minimizing sample and computational complexity. This work has implications for improving the efficiency and interpretability of machine learning models, as well as for solving problems in various fields that involve polynomial optimization or equation discovery.