Generative Framework

Generative frameworks encompass computational methods designed to create new data instances resembling a training dataset, aiming to model underlying data distributions and generate novel, realistic samples. Current research emphasizes diverse applications, from synthesizing images and videos to predicting complex systems and generating text, employing architectures like diffusion models, variational autoencoders, generative adversarial networks, and graph neural networks. These advancements have significant implications across various fields, including healthcare (predictive modeling), neuroscience (bridging data and theory), and AI safety (ensuring fairness and privacy in synthetic data generation).