GANDALF: Gated Adaptive Network for Deep Automated Learning of Features for Tabular Data
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 under MIT License.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=OE3PPhvMXQ
Changes Since Last Submission: - Added Ablation Study
- Added synthetic data based evaluation for feature importance
- Made some parts of the paper clearer to address feedback from reviewers
Assigned Action Editor: ~Vincent_Dumoulin1
Submission Number: 2720
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