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arXiv · 2309.17339

Scaling Experiments in Self-Supervised Cross-Table Representation Learning

Maximilian Schambach·Dominique Paul·Johannes S. Otterbach
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Abstract

To analyze the scaling potential of deep tabular representation learning models, we introduce a novel Transformer-based architecture specifically tailored to tabular data and cross-table representation learning by utilizing table-specific tokenizers and a shared Transformer backbone. Our training approach encompasses both single-table and cross-table models, trained via missing value imputation through a self-supervised masked cell recovery objective. To understand the scaling behavior of our method, we train models of varying sizes, ranging from approximately $10^4$ to $10^7$ parameters. These models are trained on a carefully curated pretraining dataset, consisting of 135M training tokens sourced from 76 diverse datasets. We assess the scaling of our architecture in both single-table and cross-table pretraining setups by evaluating the pretrained models using linear probing on a curated set of benchmark datasets and comparing the results with conventional baselines.

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Maximilian Schambach, Dominique Paul, Johannes S. Otterbach. 2023-09-29. Scaling Experiments in Self-Supervised Cross-Table Representation Learning. https://arxiv.org/abs/2309.17339

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