In this paper, we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [ 3 ] . Such work aims 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 using ideas from optimal transport theory. Our methods, which use the network morphism framework introduced in [ 2 ]  , can analyze forty times as many architectures as the fastest methods in the literature [ 2,10 ] while using the same computational resources and time and achieving comparable levels of accuracy. For example, with 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.
In this paper, we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [ 4 ] , which aimed at introducing the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for the optimization of a function on a semi-discrete space using ideas from optimal transport theory. Our methods, which use the network morphism framework introduced in [ 3 ] as a baseline , can analyze forty times as many architectures as the hill climbing methods [ 3,11 ] 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.
R
3
4
R
. Such work aims to introduce
, which aimed at introducing
R
2
3
A
as a baseline
R
fastest methods in the literature
hill climbing methods
R
2,10
3,11
R
with NASGD ,
using NASGD