Li et al., 2022 - Google Patents

GraphPNAS: learning distribution of good neural architectures via deep graph generative models

Li et al., 2022

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Document ID
3434580820300269580
Author
Li M
Liu J
Sigal L
Liao R
Publication year
Publication venue
arXiv preprint arXiv:2211.15155

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Snippet

Neural architectures can be naturally viewed as computational graphs. Motivated by this perspective, we, in this paper, study neural architecture search (NAS) through the lens of learning random graph models. In contrast to existing NAS methods which largely focus on …
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    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
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