Recent technological innovations along with the vast amount of available data worldwide
have led to the rise of cyberattacks against network systems. Intrusion Detection Systems …

Tabular data, organized like tables with rows and columns, is widely used. Existing models
for tabular data synthesis often face limitations related to data size or complexity. In contrast …

Denoising diffusion probabilistic models are becoming the leading generative modeling
paradigm for many important data modalities. Being the most prevalent in the computer …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

With growing attention to tabular data these days, the attempt to apply a synthetic table to
various tasks has been expanded toward various scenarios. Owing to the recent advances …

Data augmentation is an important procedure in deep learning. GAN-based data
augmentation can be utilized in many domains. For instance, in the credit card fraud domain …

The usage of synthetic data is gaining momentum in part due to the unavailability of original
data due to privacy and legal considerations and in part due to its utility as an augmentation …

Abstract Autoregressive and Masked Transformers are incredibly effective as generative
models and classifiers. While these models are most prevalent in NLP, they also exhibit …

Tabular data is a common form of organizing data. Multiple models are available to generate
synthetic tabular datasets where observations are independent, but few have the ability to …