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cs.LG2025
The Vanishing Gradient Problem for Stiff Neural Differential Equations
Colby Fronk, Linda Petzold
Gradient-based optimization of neural differential equations and other parameterized dynamical systems fundamentally relies on the ability to differentiate numerical solutions with…
cs.LG2023
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Colby Fronk, Jaewoong Yun, Prashant Singh +1
Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of man…
cs.LG2022★ 34 cited
Interpretable Polynomial Neural Ordinary Differential Equations
Colby Fronk, Linda Petzold
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. Both of th…