In addition, the authors use a standard supervised loss function for data that contain labels, an addition that makes this model applicable to semi-supervised learning. The authors propose a multi-headed self-supervised training model that first corrupts (augments) the input tabular data using a binary mask, and then one head reconstructs the mask while the other head reconstructs the uncorrupted data. Summary and Contributions: This work proposes a self-supervised framework for representation learning with tabular data. Review for NeurIPS paper: VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain NeurIPS 2020 VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain