Traditional and accelerated gradient descent for neural architecture search

1 code implementation • 26 Jun 2020 • Nicolas Garcia Trillos, Felix Morales, Javier Morales

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.

Neural Architecture Search