We propose a novel high-performance, interpretable, and parameter \&
computationally efficient deep learning architecture for tabular data, Gated
Adaptive Network for Deep Automated Learning of Features (GANDALF). GANDALF
relies on a new tabular processing unit with a gating mechanism and in-built
feature selection called Gated Feature Learning Unit (GFLU) as a feature
representation learning unit. We demonstrate that GANDALF outperforms or stays
at-par with SOTA approaches like XGBoost, SAINT, FT-Transformers, etc. by
experiments on multiple established public benchmarks. We have made available
the code at github.com/manujosephv/pytorch_tabular under MIT License.Comment: 7 pages + Reference & Appendi