Self-supervised learning on tabular data: An investigation into different implementations of VIME
With the objective to classify a tabular data set of breast cancer patients with a high accuracy the self- supervised model VIME [1] is studied. The influence of several hyperparameters during pre-training is investigated and AUC of the downstream task is regarded as the measurement of performance. A larger unlabeled synthetic data set is generated using the Synthetic Data Vault (SDV) [2]. Differe
