Scaling Experiments in Self-Supervised Cross-Table Representation Learning

• URL: http://arxiv.org/abs/2309.17339v1
• Date: Fri, 29 Sep 2023 15:48:38 GMT
• Title: Scaling Experiments in Self-Supervised Cross-Table Representation Learning
• Authors: Maximilian Schambach, Dominique Paul, Johannes S. Otterbach
• Abstract summary: We introduce a novel Transformer-based architecture specifically tailored to tabular data and cross-table representation learning. Our training approach encompasses both single-table and cross-table models, trained via missing value imputation. To understand the scaling behavior of our method, we train models of varying sizes, ranging from approximately $104$ to $107$ parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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