I am attempting to train a neural network where I can say the following:
For most inputs, I know the sign of the relationship between that input and several specific outputs. I.e. whatever set of values the inputs are set to, I can point to individual input and say that so long as the other inputs are unchanged, the output should monotonically increase/decrease as I change this input's value.
This is not to say that the inputs don't interact with each other, they do, but the interaction never changes the sign (i.e. increasing vs decreasing) nature of the monotonic relationships between specific inputs and outputs.
I am wondering how I can use this information to help my network learn fast and/or perform well.