  The emergence of neural architecture search (NAS) has greatly advanced the
research on network design. Recent proposals such as gradient-based methods or
one-shot approaches significantly boost the efficiency of NAS. In this paper,
we formulate the NAS problem from a Bayesian perspective. We propose explicitly
estimating the joint posterior distribution over pairs of network architecture
and weights. Accordingly, a hybrid network representation is presented which
enables us to leverage the Variational Dropout so that the approximation of the
posterior distribution becomes fully gradient-based and highly efficient. A
posterior-guided sampling method is then presented to sample architecture
candidates and directly make evaluations. As a Bayesian approach, our
posterior-guided NAS (PGNAS) avoids tuning a number of hyper-parameters and
enables a very effective architecture sampling in posterior probability space.
Interestingly, it also leads to a deeper insight into the weight sharing used
in the one-shot NAS and naturally alleviates the mismatch between the sampled
architecture and weights caused by the weight sharing. We validate our PGNAS
method on the fundamental image classification task. Results on Cifar-10,
Cifar-100 and ImageNet show that PGNAS achieves a good trade-off between
precision and speed of search among NAS methods. For example, it takes 11 GPU
days to search a very competitive architecture with 1.98% and 14.28% test
errors on Cifar10 and Cifar100, respectively.
