Flexible, Non-parametric Modeling Using Regularized Neural Networks. (arXiv:2012.11369v3 [cs.LG] UPDATED)

Non-parametric, additive models are able to capture complex data dependencies
in a flexible, yet interpretable way. However, choosing the format of the
additive components often requires non-trivial data exploration. Here, as an
alternative, we propose PrAda-net, a one-hidden-layer neural network, trained
with proximal gradient descent and adaptive lasso. PrAda-net automatically
adjusts the size and architecture of the neural network to reflect the
complexity and structure of the data. The compact network obtained by PrAda-net
can be translated to additive model …

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