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Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
[article]
2020
arXiv
pre-print
Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. These methods enjoy great advantages in terms of computational costs, but the sampled sub-networks are not guaranteed to be estimated precisely unless an individual training process
arXiv:2004.08423v2
fatcat:jak2qi2wm5ffbflnpylyfq2txq