I looked around online but couldn't find anything, but I may well have missed a piece of literature on this. I am running a basic neural net on a 289 component vector to produce a 285 component vector. In my input, the last 4 pieces of data are critical to change the rest of the input into the resultant 285 for the output. That is to say, the input is 285 + 4, such that the 4 morph the rest of the input into the output.
But when running a neural network on this, I am not sure how to reflect this. Would I need to use convolution on the rest of the input? I want my system to emphasize the 4 data points that critically affect the other 285. I am still new to all of this, so a few pointers would be great!
Again, if there is something already written on this, then that would be awesome too.