Correcting auto-differentiation in neural-ODE training
Yewei Xu Shi Chen Qin Li Stephen J. Wright
Does the use of auto-differentiation yield reasonable updates to deep neural
networks that represent neural ODEs? Through mathematical analysis and
numerical evidence, we find that when the neural network employs high-order
forms to approximate the underlying ODE flows (such as the Linear Multistep
Method (LMM)), brute-force computation using auto-differentiation often
produces non-converging artificial oscillations. In the case of Leapfrog, we
propose a straightforward post-processing technique that effectively eliminates
these oscillations, rectifies the gradient computation and thus respects the
updates of the underlying flow.