Hey folks. I have been working on creating a Neural ODE and a Universal ODE for a 6 compartment model. One common neural network practice is to normalize the inputs to be between 0 and 1. For example in a convolutional neural net, the 3 channel 8bit image with color range from 0 to 255 per channel is normalized to the range of 0,1 per channel. In a Neural ODE or Universal ODE context this means normalizing the actual training data to be between 0,1 for each group. As a result of normalizing, I would also obtain outputs or predictions that were between 0,1.
Now in looking at the Universal ODE github repo, and the examples for NeuralODE in DiffEqFlux, I have not seen anyone normalize their Neural ODE/ Universal ODE training data and predictions. Hence I was wondering if anyone knows whether normalizing helps the performance of these Neural ODE models, or if it makes no difference. Thanks.