[D] Model architecture to test and compare hyperparameter tuning methods
I’m looking for a model/dataset that is fast to train (e.g. comparable to logistic regression on MNIST) that can be used to test and compare various hyperparameter search methods without a GPU/accelerator. Model performance should be sensitive to 2-3 hyperparameters. My experience with tuning has been with neural networks that are very time consuming to train on non-accelerated systems.