Towards a configurable and non-hierarchical search space for NAS

Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can be cumbersome, and it is hard to know which of the...

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Vydané v:	Neural networks Ročník 180; s. 106700
Hlavní autori:	Perrin, Mathieu, Guicquero, William, Paille, Bruno, Sicard, Gilles
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.12.2024
Predmet:	Algorithms Bayes Theorem Bayesian optimization Convolutional neural network Humans Neural architecture search Neural Networks, Computer Normal Distribution Wasserstein autoencoder Wasserstein autoencoder Neural architecture search Bayesian optimization Convolutional neural network
ISSN:	0893-6080, 1879-2782, 1879-2782
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