Model Performance Prediction for Hyperparameter Optimization of Deep
  Learning Models Using High Performance Computing and Quantum Annealing
Juan Pablo García Amboage Eric Wulff Maria Girone Tomás F. Pena
Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be a
compute resource intensive process as it usually requires to train the target
model with many different hyperparameter configurations. We show that
integrating model performance prediction with early stopping methods holds
great potential to speed up the HPO process of deep learning models. Moreover,
we propose a novel algorithm called Swift-Hyperband that can use either
classical or quantum support vector regression for performance prediction and
benefit from distributed High Performance Computing environments. This
algorithm is tested not only for the Machine-Learned Particle Flow model used
in High Energy Physics, but also for a wider range of target models from
domains such as computer vision and natural language processing.
Swift-Hyperband is shown to find comparable (or better) hyperparameters as well
as using less computational resources in all test cases.