Isn’t it structurally similar to an RNN, because we are feeding the output of the convolution into itself over a number of steps in sequence? I thought pytorch might even use BPTT for this kind of setup.
If my input grid’s length is variable and I want to communicate information to cell (i,j) from every other cell in the grid, or 50% of the other cells in the grid, etc., then I cannot just use multiple convolution layers, I need a way to apply a variable number of convolutions.