Very interesting - I mean I know that in the context of RNNs we can have variable length inputs etc, since the back-prop can be unrolled N times, but I didnt realize that we could have variable length weights/parameters etc… I’ll read more about it, (and any sources on this are appreciated), but the top question in my mind is how do you guarantee that in a dynamic graph, when we change the dimensionality of weights, that those are new weights are even trained? Does that make sense?