In this work we present a novel approach to network embedding for neural architecture search - info-NAS. The model learns to predict the output fea- tures of a trained convolutional neural network on a set of input images. We use the NAS-Bench-101 search space as the neural architecture dataset, and the CIFAR-10 as the image dataset. For the purpose of this task, we extend an existing unsupervised graph variational autoencoder, arch2vec, by jointly training on unlabeled and labeled neural architectures in a semi-supervised manner. To evaluate our approach, we analyze how our model learns on the data, compare it to the original arch2vec, and finally, we evaluate both mod- els on the NAS-Bench-101 search task and on the performance prediction task. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:451129 |
| Date | January 2021 |
| Creators | Suchopárová, Gabriela |
| Contributors | Neruda, Roman, Pilát, Martin |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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