In this paper we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [5] which used the geometric structure of optimal transport to introduce the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for the optimization of a function on a semi-discrete space. Our algorithms, which use the network morphism framework introduced in [2] as a baseline, can analyze forty times as many architectures as the hill climbing methods [2, 14] while using the same computational resources and time and achieving comparable levels of accuracy. For example, using NASGD on CIFAR-10, our method designs and trains networks with an error rate of 4.06 in only 12 hours on a single GPU.
@article{arxiv.2006.15218,
title = {Traditional and accelerated gradient descent for neural architecture search},
author = {Nicolas Garcia Trillos and Felix Morales and Javier Morales},
journal= {arXiv preprint arXiv:2006.15218},
year = {2021}
}