Supplementary Materials - VIME: Extending the Success of Self-and Semi-supervised Learning to Tabular Domain

Self-supervised learning trains an encoder to extract informative representations on the unlabeled data. Semisupervised learning uses the trained encoder in learning a predictive model on both labeled and unlabeled data. Figure 3: The proposed data corruption procedure. In the experiment section of the main manuscript, we evaluate VIME and its benchmarks on 11 datasets (6 genomics, 2 clinical, and 3 public datasets). Here, we provide the basic data statistics for the 11 used datasets in Table 1.