A note on the adjoint method for neural ordinary differential equation network

arXiv:2402.15141v1 Announce Type: cross
Abstract: Perturbation and operator adjoint method are used to give the right adjoint form rigourously. From the derivation, we can have following results: 1) The loss gradient is not an ODE, it is an integral and we shows the reason; 2) The traditional adjoint form is not equivalent with the back propagation results. 3) The adjoint operator analysis shows that if and only if the discrete adjoint has the same scheme with the discrete neural ODE, …

abstract arxiv cs.lg cs.na derivation differential differential equation equation form gradient integral loss math.na network ordinary reason results shows type