  Modern Neural Architecture Search methods have repeatedly broken
state-of-the-art results for several disciplines. The super-network, a central
component of many such methods, enables quick estimates of accuracy or loss
statistics for any architecture in the search space. They incorporate the
network weights of all candidate architectures and can thus approximate
specific ones by applying the respective operations. However, this design
ignores potential dependencies between consecutive operations. We extend
super-networks with conditional weights that depend on combinations of choices
and analyze their effect. Experiments in NAS-Bench 201 and
NAS-Bench-Macro-based search spaces show improvements in the architecture
selection and that the resource overhead is nearly negligible for sequential
network designs.
