It's mentioned briefly in the paper(1), but I'm more interested in the interpretability implications of this approach. In some respects, this marries the interp

It's mentioned briefly in the paper(1), but I'm more interested in the interpretability implications of this approach. In some respects, this marries the interpretability/editability of a small decision tree with the expressive power of a large neural network. Usually you see those two on extreme opposite ends of a tradeoff spectrum - but this approach, if it scales, might shift the pareto frontier. (1): As a byproduct, the learned regions can also be used as a partition of the input space for interpretability, surgical model editing, catastrophic forgetting mitigation, reduction of replay data budget, etc..