Abstract

Artificial neural networks (NNs) in deep learning systems are critical drivers of emerging technologies such as computer vision, text classification, and natural language processing. Fundamental to their success is the development of accurate and efficient NN models. In this article, we report our work on Deep-n-Cheap—an open-source automated machine learning (AutoML) search framework for deep learning models. The search includes both architecture and training hyperparameters and supports convolutional neural networks and multi-layer perceptrons, applicable to multiple domains. Our framework is targeted for deployment on both benchmark and custom datasets, and as a result, offers a greater degree of search space customizability as compared to a more limited search over only pre-existing models from literature. We also introduce the technique of ‘search transfer’, which demonstrates the …

Date

2021

Authors

Sourya Dey, Sara Babakniya, Saikrishna C Kanala, Marco Paolieri, Leana Golubchik, Peter A Beerel, Keith M Chugg

Journal

SN Computer Science

Volume

2

Issue

4

Pages

265

Publisher

Springer Singapore