Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to efficiently explore large hyperparameter search space due to the long training times of modern CNNs. Multi-fidelity optimization enables the exploration of more hyperparameter configurations given budget by …