Papers › GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

18 Jul 2022arXiv:2207.08548archive 2025-07-28

Manu Joseph, Harsh Raj

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.

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Methods

Introduced by this paper: GANDALF

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