Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs), one of the most influential works of the
differential equation-based deep learning, are to continuously generalize residual networks …
differential equation-based deep learning, are to continuously generalize residual networks …
PIDNODEs: Neural ordinary differential equations inspired by a proportional–integral–derivative controller
Abstract Neural Ordinary Differential Equations (NODEs) are a novel family of infinite-depth
neural-net models through solving ODEs and their adjoint equations. In this paper, we …
neural-net models through solving ODEs and their adjoint equations. In this paper, we …
Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) are an emerging paradigm in scientific
computing for modeling dynamical systems. By accurately learning underlying dynamics in …
computing for modeling dynamical systems. By accurately learning underlying dynamics in …
[图书][B] Towards Faster and More Accurate Neural ODEs
H Xia - 2023 - search.proquest.com
Abstract Neural Ordinary Differential Equations (NODEs) have improved accuracy and
memory efficiency over general deep neural networks but suffer from the vanishing gradient …
memory efficiency over general deep neural networks but suffer from the vanishing gradient …
When Neural ODEs meet Neural Operators
Differential equation-based neural networks perform well in a variety of deep learning fields.
Among those many methods, neural ordinary differential equations (NODEs) are one of the …
Among those many methods, neural ordinary differential equations (NODEs) are one of the …