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。
数据增强(Data Augmentation)是一种使用少量数据通过先验知识产生更多的相似生成数据来扩展训练数据集的方法。数据增强方法常用于解决现实业务中的小样本问题,参考小样本学习分享。 小样本学习主要问题是样本量过少,从而导致样本多样性不足以刻画完整样本分布,可以通过样本增强来提升样本多样性;基于数据增强的方法是利用辅助...
However, there are no standardized data augmentation processes that can be applied to every domain of tabular data. Therefore, this study aims to identify which characteristics of a dataset provide a better performance when synthesizing samples by a data augmentation technique in a tabular data ...
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...
♣️Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI. A survey providing a comprehensive examination of tabular data augmentation (TDA) methods tailored for ML scenarios, with a special emphasis on the recent advancements in incorporating generative AI ...
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 Augmentation One of the main applications of CGANs in tabular data modeling is data augmentation. 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...
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...
Mix-up [23] is an augmentation method that creates new examples as convex combinations of the original training samples. Given a dataset with labeled examples, Mix-up combines pairs of input samples (both the features and labels) by taking a weighted linear combination. ...
The DACMVA framework of cross-modal variational eutoencoders (CM-VAE) with various forms of data augmentation was assessed using a real-world cancer survival prediction test using tabular data. First, the imputation quality of the DACMVA models was compared to TDImpute [11]. Next, the effect...