  Despite their impressive performance, Deep Neural Networks (DNNs) typically
underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning
tasks. We propose that applying a different regularization coefficient to each
weight might boost the performance of DNNs by allowing them to make more use of
the more relevant inputs. However, this will lead to an intractable number of
hyperparameters. Here, we introduce Regularization Learning Networks (RLNs),
which overcome this challenge by introducing an efficient hyperparameter tuning
scheme which minimizes a new Counterfactual Loss. Our results show that RLNs
significantly improve DNNs on tabular datasets, and achieve comparable results
to GBTs, with the best performance achieved with an ensemble that combines GBTs
and RLNs. RLNs produce extremely sparse networks, eliminating up to 99.8% of
the network edges and 82% of the input features, thus providing more
interpretable models and reveal the importance that the network assigns to
different inputs. RLNs could efficiently learn a single network in datasets
that comprise both tabular and unstructured data, such as in the setting of
medical imaging accompanied by electronic health records. An open source
implementation of RLN can be found at
https://github.com/irashavitt/regularization_learning_networks.
