GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

Autor:	Joseph, Manu, Raj, Harsh
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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: 15 pages + Reference & Appendix
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.08548 Zobrazit plný text záznamu View this record from Arxiv