Graph Embedding for Neural Architecture Search with Input-Output Information

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00560713" target="_blank" >RIV/67985807:_____/22:00560713 - isvavai.cz</a>

Result on the web

—

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Graph Embedding for Neural Architecture Search with Input-Output Information

Original language description

ZÁKLADNÍ ÚDAJE: Auto-ML Conf 2022: Accepted Papers: Late-Breaking Workshop. Baltimore: AutoML Conference, 2022. KONFERENCE: Auto-ML 2022: International Conference on Automated Machine Learning /1./. Baltimore (US), 25.07.2022-27.07.2022. ABSTRAKT: Graph representation learning has been widely used in neural architecture search as a part of performance prediction models. Existing works focused mostly on neural graph similarity without considering functionally similar networks with different architectures. In this work, we address this issue by using meta-information of input images and output features of a particular neural network. We extended the arch2vec model, a graph variational autoencoder for neural architecture search, to learn from this novel kind of data in a semi-supervised manner. We demonstrate our approach on the NAS-Bench-101 search space and the CIFAR10 dataset, and compare our model with the original arch2vec on a REINFORCE search task and a performance prediction task. We also present a semi-supervised accuracy predictor, and we discuss the advantages of both variants. The results are competitive with the original model and show improved performance.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů