论文标题
图形卷积神经常规微分方程的矢量化伴随灵敏度方法
Vectorized Adjoint Sensitivity Method for Graph Convolutional Neural Ordinary Differential Equations
论文作者
论文摘要
如标题所述，该文档旨在为图形卷积神经普通微分方程（GCDE）提供媒介实现的矢量化实现。伴随灵敏度方法是替代背部传播的神经ODE的梯度近似方法。当在Pytorch或Tensorflow等库上实施时，可以通过自动式函数计算伴随，而无需手工衍生的公式。但是，在Edge Computing和Memristor Crossbars之类的应用中，自autograds不可用，因此我们需要对伴随动力学的矢量推导来有效地在硬件上映射系统。该文档将介绍基础知识，然后继续进行GCDE的矢量伴随动力学。
This document, as the title stated, is meant to provide a vectorized implementation of adjoint dynamics calculation for Graph Convolutional Neural Ordinary Differential Equations (GCDE). The adjoint sensitivity method is the gradient approximation method for neural ODEs that replaces the back propagation. When implemented on libraries such as PyTorch or Tensorflow, the adjoint can be calculated by autograd functions without the need for a hand-derived formula. In applications such as edge computing and in memristor crossbars, however, autograds are not available, and therefore we need a vectorized derivation of adjoint dynamics to efficiently map the system on hardware. This document will go over the basics, then move on to derive the vectorized adjoint dynamics for GCDE.
