GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

• URL: http://arxiv.org/abs/2207.08548v6
• Date: Wed, 10 Jan 2024 00:28:04 GMT
• Title: GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
• Authors: Manu Joseph, Harsh Raj
• Abstract summary: 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) We demonstrate that GANDALF outperforms or stays at-par with SOTA approaches like XGBoost, SAINT, FT-Transformers, etc.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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