8 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 8 cited
Rethinking Data Augmentation for Tabular Data in Deep Learning
Soma Onishi, Shoya Meguro
Tabular data is the most widely used data format in machine learning (ML). While tree-based methods outperform DL-based methods in supervised learning, recent literature reports th…
cs.LG2023
TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns
Soma Onishi, Kenta Oono, Kohei Hayashi
We present \emph{TabRet}, a pre-trainable Transformer-based model for tabular data. TabRet is designed to work on a downstream task that contains columns not seen in pre-training.…