Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity

no code implementations • 25 Feb 2022 • Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett

In order to empower NAM with feature selection and improve the generalization, we propose the sparse neural additive models (SNAM) that employ the group sparsity regularization (e. g. Group LASSO), where each feature is learned by a sub-network whose trainable parameters are clustered as a group.

Additive models feature selection +1