GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866916086310502400 |
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| author | Joseph, Manu Raj, Harsh |
| author_facet | Joseph, Manu Raj, Harsh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_08548 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | GANDALF: Gated Adaptive Network for Deep Automated Learning of Features Joseph, Manu Raj, Harsh Machine Learning 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. |
| title | GANDALF: Gated Adaptive Network for Deep Automated Learning of Features |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2207.08548 |