Methods › General › Deep Tabular Learning › GANDALF

Gated Adaptive Network for Deep Automated Learning of Features

GANDALF

4 papers tagged archive 2025-07-28

Introduced by Manu Joseph et al. in GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

Paper Source

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks
20 Jan 2025 · 0 repositories · arXiv:2501.11762
A Survey on Deep Tabular Learning
15 Oct 2024 · 0 repositories · arXiv:2410.12034
Learning label-label correlations in Extreme Multi-label Classification via Label Features
3 May 2024 · 0 repositories · arXiv:2405.04545
GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
18 Jul 2022 · 2 repositories · arXiv:2207.08548

Syntology ran 2 of 14 samples · 12 unverified

Tasks archive 2025-07-28

16 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
feature selection	2
Denoising	1
Dimensionality Reduction	1
Extreme Multi-Label Classification	1
MUlTI-LABEL-ClASSIFICATION	1
Multi Label Text Classification	1
Multi-Label Classification	1
Multi-Label Text Classification	1
Product Recommendation	1
Representation Learning	1
Survey	1
Text Classification	1
Transfer Learning	1
regression	1
tabular-classification	1
text-classification	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Deep Tabular Learning

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