This paper extends recent work on decision rule learning from neural networks for tabular data classification. We propose alternative formulations to trainable Boolean logic operators as neurons with continuous weights, including NAND neurons. These provide uniform treatments different so that they can be uniformly trained, which enables, example, the direct application of existing sparsity-pro...
