Search Results for author:
Found 2 papers, 0 papers with code
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems.
Interpretable Polynomial Neural Ordinary Differential Equations
Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
