Papers listed here may be not from top publications, some of them even are not for purely relational data, but are all interesting papers related to relational data augmentation that deserve reading.Year 2023[SIGMOD] SANTOS: Relationship-based Semantic Table Union Searchtaset Discovery from Data La...
今天介绍一个我们的新工作TapTap,第一个通过大规模tabular data上预训练的语言模型来提升机器学习模型预测效果的工作。在预训练之后,TapTap可以合成高质量的tabular data,从而通过支持data augmentation, missing value imputation, imbalanced classification, 和privacy protection等多个应用场景来提升机器学习模型的预测效果。
Post-augmentation.md Pre-augmentation.md README.md Breadcrumbs awesome-tabular-data-augmentation / README.md Latest commit Cannot retrieve latest commit at this time. HistoryHistory File metadata and controls Preview Code Blame 31 lines (12 loc) · 892 Bytes Raw ♣️ Tabular Data Augmentati...
For data augmentation, we conducted a progressive analysis of the imbalance ratio (IR), related to the amount of synthetic samples created, and evaluated the impact on the predictive results. To gain interpretability on predictive models, we used SHAP, bootstrap resampling statistical tests and ...
TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting Tabular data is prevalent in many critical domains, yet it is often challenging to acquire in large quantities. This scarcity usually results in poor perfo... A Margeloiu,A Bazaga,N Simidji...
Tabular data is prevalent in many critical domains, yet it is often challenging to acquire in large quantities. This scarcity usually results in poor performance of machine learning models on such data. Data augmentation, a common strategy for performance improvement in vision and language tasks, ty...
data的feature在上千维,可操作性会强一点,比如swapnoise的比例低一些,不太容易改变原始sample的语义,但是如果input size本身就非常稀少,例如只有10维,则还是很容易通过改变哪怕一个feature就改变原始sample的语义(关于tabular data的数据增强的研究确实比较少也缺乏系统性,https://github.com/zhxfei/tabular_augmentation...
Mix-up [23] is a data augmentation technique commonly used in CV for tasks such as image classification. It was introduced as a regularization method to improve the generalization and robustness of models, especially in scenarios with limited labeled data. Combining different features and labels wit...
We consider the task of self-supervised representation learning (SSL) for tabular data: tabular-SSL. Typical contrastive learning based SSL methods require instance-wise data augmentations which are difficult to design for unstructured tabular data. Existing tabular-SSL methods design such augmentations...
Data augmentation refers to the process of expanding the size of the training dataset by generating new synthetic samples. By conditioning the generator on the existing data and additional attribute or label information, CGANs can generate realistic synthetic data that captures the underlying patterns ...