A wrapper function signature might look something like dataset_similarity (p_file, q_file, ae_ld, ae_bs, ae_me) where ae_ld is the autoencoder latent dimension size, ae_bs is the autoencoder training ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

The autoencoder network model for HIV classification, proposed in this paper, thus outperforms the conventional feedforward neural network models and is a much better classifier.