Summary of A Survey on Self-supervised Learning For Non-sequential Tabular Data, by Wei-yao Wang et al.
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
by Wei-Yao Wang, Wei-Wei Du, Derek Xu, Wei Wang, Wen-Chih Peng
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper surveys recent progress and challenges in self-supervised learning (SSL) for non-sequential tabular data (NS-TD), which involves learning contextualized and robust representations from unlabeled datasets. The authors categorize existing approaches into predictive, contrastive, and hybrid learning methods, highlighting their strengths and limitations. They also discuss application issues such as automatic data engineering, cross-table transferability, and domain knowledge integration. Finally, they elaborate on existing benchmarks and datasets for NS-TD applications to analyze the performance of tabular models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SSL helps models learn from unlabeled data by defining pretext tasks that help them become better at understanding tables without explicit relationships between columns. This paper looks at how SSL works in this context, grouping methods into three categories: predictive learning, contrastive learning, and hybrid learning. The authors also talk about challenges like automatic data engineering and transferring knowledge across different tables. |
Keywords
* Artificial intelligence * Self supervised * Transferability
