TY - GEN
T1 - Hyper-parameter selection in deep neural networks using parallel particle swarm optimization
AU - Lorenzo, Pablo Ribalta
AU - Nalepa, Jakub
AU - Ramos, Luciano Sanchez
AU - Pastor, Jośe Ranilla
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/7/15
Y1 - 2017/7/15
N2 - The need of manual hyper-parameter selection can seriously hamper the model optimization of Deep Neural Networks (DNNs). Conventional automated approaches tackling this problem su.er from poor scalability or fail in certain scenarios. In this paper, we introduce a parallel method that applies Particle Swarm Optimization (PSO) for the hyper-parameter selection in DNNs. To estimate the best hyper-parameters, a population of particles is evolved, with their .tness calculated in parallel. .e experimental results demonstrate very desirable scalability properties for different DNNs. We show that the parallel PSO can further optimize existent models designed by experts in an affordable amount of time.
AB - The need of manual hyper-parameter selection can seriously hamper the model optimization of Deep Neural Networks (DNNs). Conventional automated approaches tackling this problem su.er from poor scalability or fail in certain scenarios. In this paper, we introduce a parallel method that applies Particle Swarm Optimization (PSO) for the hyper-parameter selection in DNNs. To estimate the best hyper-parameters, a population of particles is evolved, with their .tness calculated in parallel. .e experimental results demonstrate very desirable scalability properties for different DNNs. We show that the parallel PSO can further optimize existent models designed by experts in an affordable amount of time.
KW - Deep Neural Networks
KW - Hyper-Parameter Selection
KW - Parallel Evolutionary Algorithm
KW - Particle Swarm Optimization
UR - https://www.scopus.com/pages/publications/85026858152
U2 - 10.1145/3067695.3084211
DO - 10.1145/3067695.3084211
M3 - Conference contribution
AN - SCOPUS:85026858152
T3 - GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
SP - 1864
EP - 1871
BT - GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
Y2 - 15 July 2017 through 19 July 2017
ER -