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UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
March 14, 2024, 4:43 a.m. | Yazheng Yang, Yuqi Wang, Guang Liu, Ledell Wu, Qi Liu
cs.LG updates on arXiv.org arxiv.org
Abstract: Recent advancements in NLP have witnessed the groundbreaking impact of pretrained models, yielding impressive outcomes across various tasks. This study seeks to extend the power of pretraining methodologies to facilitating the prediction over tables in data science, a domain traditionally overlooked, yet inherently challenging due to the plethora of table schemas intrinsic to different tasks. The primary research questions underpinning this work revolve around the establishment of a universal pretraining protocol for tables with varied …
abstract arxiv cs.ai cs.cl cs.lg data data science domain foundation foundation model groundbreaking impact nlp power prediction pretrained models pretraining protocol science study tables tabular tasks type universal
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