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.

利用模型性能预测与提前停止方法相结合，加速深度学习模型的超参数优化过程，通过 Swift-Hyperband 算法在高性能计算环境中测试，发现它在高能物理、计算机视觉和自然语言处理等领域的各种目标模型中找到相当好的超参数，且使用较少的计算资源。