论文标题
甘道夫：用于深度自动化功能的封闭自适应网络
GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
论文作者
论文摘要
我们提出了一个新颖的高性能，可解释的和参数\＆计算有效的深度学习架构，用于表格数据，用于特征的深度自动化学习（Gandalf）的门控自适应网络（Gandalf）。甘道夫依靠一个具有门控机制的新表格处理单元，并依靠称为门的特征学习单元（GFLU）作为特征表示学习单元。我们证明，通过在多个已建立的公共基准的实验中，甘道夫的表现优于Xgboost，Saint，Ft-Transformers等SOTA方法。我们已经在MIT许可证的github.com/manujosephv/pytorch_tabular上提供了代码。
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.
