May 14, 2024, 4:42 a.m. | Kyungeun Lee, Ye Seul Sim, Hye-Seung Cho, Moonjung Eo, Suhee Yoon, Sanghyu Yoon, Woohyung Lim

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07414v1 Announce Type: new
Abstract: The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains, it is critical to effectively handle heterogeneous features (both categorical and numerical) in a unified manner and to grasp irregular functions like piecewise constant functions. To address the challenges in the self-supervised learning framework, we propose a novel pretext task based on the classical binning method. The idea is straightforward: …

arxiv cs.ai cs.lg domains improving self-supervised learning supervised learning tabular type

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