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arXiv · 2311.07343

Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning

Felix den Breejen·Sangmin Bae·Stephen Cha·Tae-Young Kim·Seoung Hyun Koh·Se-Young Yun
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Abstract

While interests in tabular deep learning has significantly grown, conventional tree-based models still outperform deep learning methods. To narrow this performance gap, we explore the innovative retrieval mechanism, a methodology that allows neural networks to refer to other data points while making predictions. Our experiments reveal that retrieval-based training, especially when fine-tuning the pretrained TabPFN model, notably surpasses existing methods. Moreover, the extensive pretraining plays a crucial role to enhance the performance of the model. These insights imply that blending the retrieval mechanism with pretraining and transfer learning schemes offers considerable potential for advancing the field of tabular deep learning.

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Felix den Breejen, Sangmin Bae, Stephen Cha, Tae-Young Kim, Seoung Hyun Koh, Se-Young Yun. 2023-11-13. Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning. https://arxiv.org/abs/2311.07343

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