Evolutionary neural cascade search across supernetworks

Genetic and Evolutionary Computation Conference (GECCO), pp. 1038–1047

Abstract

To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage super-networks whose subnetworks encode candidate neural network architectures. These subnetworks can be trained simultaneously, removing the need to train each network from scratch, thereby increasing the efficiency of NAS.

Authors 3

1. Alexander Chebykin

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

2. Tanja Alderliesten Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, the Netherlands

3. Peter A. N. Bosman

   Delft University of Technology

   Affiliation as printed

   TU Delft, Delft, The Netherlands