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DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
[article]
2019
arXiv
pre-print
Automatic search of neural network architectures is a standing research topic. In addition to the fact that it presents a faster alternative to hand-designed architectures, it can improve their efficiency and for instance generate Convolutional Neural Networks (CNN) adapted for mobile devices. In this paper, we present a multi-objective neural architecture search method to find a family of CNN models with the best accuracy and computational resources tradeoffs, in a search space inspired by the
arXiv:1902.01654v1
fatcat:r7podmzsdzgqbjse3c7dfv36re