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$\mu$DARTS: Model Uncertainty-Aware Differentiable Architecture Search. (arXiv:2107.11500v2 [cs.LG] UPDATED)
Sept. 13, 2022, 1:12 a.m. | Biswadeep Chakraborty, Saibal Mukhopadhyay
cs.LG updates on arXiv.org arxiv.org
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 …
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