neural architecture search (NAS) is a challenging problem. Hierarchical
search spaces allow for cheap evaluations of neural network sub modules to
serve as surrogate for architecture evaluations. Yet, sometimes the hierarchy
is too restrictive or the surrogate fails to generalize. We p