A neural network with feature sparsity

This technology has provided near-human performance on many prediction tasks Geirhos et al. (2017), and left deep marks on entire fields of business and science, to the extent that large computational and engineering efforts are routinely dedicated to neural network training and optimization Dean et al. (2012). However, neural networks are often criticized for their complexity and lack of interpretability. There are many arguments that favor simple models over more complex ones. In many applications (including healthcare Ahmad et al. (2018), Cabitza et al. (2017), insurance and finance Song et al. (2014), Thomas et al. (2002), flight control and other safety-critical tasks (Kurd et al. (2007)), interpretation of the underlying model is a critical requirement. On the other hand, traditional statistical tools, including simple linear models, remain popular because they are simple and explainable, with cheap, efficient computational tools being readily available.