Comment by fwlr

They introduce “height” as another architectural dimension, alongside the usual width and depth. If you imagine the usual diagram of a neural network, the difference when a neural net is of height 2 is that in the middle layers, each individual node contains another network inside it, and that inner network has the same structure as the top-level network. For height 3, each node has an inner network, and each of those inner networks is composed of nodes that have their own inner networks as well. And so on, recursively, for greater heights. There’s a diagram on page 3.