
There is usually a significant interaction between different parameters, especially for techniques that focus on Regularization and reducing overfitting. Thus changing the value of one can have effects on the optimal configurations of others. To account for that one can either adopt a greedy search approach (which often leads to suboptimal results but tractable experiments) or apply grid search (which leads to better results but is computationally expensive). In this work, we used a mixture of both.
Techniques that are non-deterministic or introduce noise usually require longer training cycles to improve model performance. To keep things tractable, we initially used short training cycles (small number of epochs) to decide which paths can be eliminated early and which should be explored using longer training.
