Over the past few years, adversarial training has become an extremely active research topic
and has been successfully applied to various Artificial Intelligence (AI) domains. As a …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Generative adversarial networks (GANs) implicitly learn the probability distribution of a
dataset and can draw samples from the distribution. This paper presents, Tabular GAN …

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied
across diverse domains. However, progress is impeded by the scarcity of training data due …

Real-world applications often involve imbalanced datasets, which have different
distributions of examples across various classes. When building a system that requires a …

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art
technologies engaging artificial intelligence. The unavailability of quality data due to both …

Synthetic data generation for tabular data unlike computer vision, is an emerging challenge.
When tabular data needs to be synthesized, it either faces a small dataset problem or …

Credit card churn prediction, insurance fraud detection, and loan default prediction are all
critical analytical customer relationship management (ACRM) problems. Since these events …

Data generation techniques are one of the emerging trends in machine learning in the last
decade. Despite huge data availability, small datasets are still an issue to tackle for decision …

Synthesizing tabular data is attracting much attention these days for various purposes. With
sophisticate synthetic data, for instance, one can augment its training data. For the past …