Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Accessible high-quality data is the bread and butter of machine learning research, and the
demand for data has exploded as larger and more advanced ML models are built across …

Synthetic data serves as an alternative in training machine learning models, particularly
when real-world data is limited or inaccessible. However, ensuring that synthetic data …

Data is the fuel of machine learning algorithms, therefore data generation in machine
learning is becoming an important topic. The problem is that finding enough data for …

Generating synthetic data through generative models is gaining interest in the ML
community and beyond. In the past, synthetic data was often regarded as a means to private …

Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of
healthcare data for secondary purposes. It is considered a safe approach for the sharing of …

Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …

Deep learning models have demonstrated high-quality performance in areas such as image
classification and speech processing. However, creating a deep learning model using …

Large language models (LLMs) have great potential for synthetic data generation. This work
shows that useful data can be synthetically generated even for tasks that cannot be solved …