Interpretable Mesomorphic Neural Networks For Tabular Data

Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this work, we propose a new class of interpretable neural networks for tabular data that are both deep and linear at the same time (i.e.