  Neural architecture search (NAS) has recently been addressed from various
directions, including discrete, sampling-based methods and efficient
differentiable approaches. While the former are notoriously expensive, the
latter suffer from imposing strong constraints on the search space.
Architecture optimization from a learned embedding space for example through
graph neural network based variational autoencoders builds a middle ground and
leverages advantages from both sides. Such approaches have recently shown good
performance on several benchmarks. Yet, their stability and predictive power
heavily depends on their capacity to reconstruct networks from the embedding
space. In this paper, we propose a two-sided variational graph autoencoder,
which allows to smoothly encode and accurately reconstruct neural architectures
from various search spaces. We evaluate the proposed approach on neural
architectures defined by the ENAS approach, the NAS-Bench-101 and the
NAS-Bench-201 search space and show that our smooth embedding space allows to
directly extrapolate the performance prediction to architectures outside the
seen domain (e.g. with more operations). Thus, it facilitates to predict good
network architectures even without expensive Bayesian optimization or
reinforcement learning.
