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DC-NAS: Divide-and-Conquer Neural Architecture Search
[article]
2020
arXiv
pre-print
Most applications demand high-performance deep neural architectures costing limited resources. Neural architecture searching is a way of automatically exploring optimal deep neural networks in a given huge search space. However, all sub-networks are usually evaluated using the same criterion; that is, early stopping on a small proportion of the training dataset, which is an inaccurate and highly complex approach. In contrast to conventional methods, here we present a divide-and-conquer (DC)
arXiv:2005.14456v1
fatcat:4pledk4txjefhg4tu6gz2h5v7i