  We present a Model Uncertainty-aware Differentiable ARchiTecture Search
($\mu$DARTS) that optimizes neural networks to simultaneously achieve high
accuracy and low uncertainty. We introduce concrete dropout within DARTS cells
and include a Monte-Carlo regularizer within the training loss to optimize the
concrete dropout probabilities. A predictive variance term is introduced in the
validation loss to enable searching for architecture with minimal model
uncertainty. The experiments on CIFAR10, CIFAR100, SVHN, and ImageNet verify
the effectiveness of $\mu$DARTS in improving accuracy and reducing uncertainty
compared to existing DARTS methods. Moreover, the final architecture obtained
from $\mu$DARTS shows higher robustness to noise at the input image and model
parameters compared to the architecture obtained from existing DARTS methods.
