Energy Based Generative

Energy-based generative models (EBMs) aim to learn probability distributions by defining an energy function, where lower energy corresponds to higher probability. Current research focuses on improving training efficiency and stability, exploring novel architectures like variational potential flows and integrating EBMs with other techniques such as transformers and normalizing flows to handle diverse data types, including images and tabular data. These advancements are impacting various fields, enabling improved generative capabilities for tasks like data augmentation, anomaly detection, and even drug discovery by generating molecules with desired properties. The development of more efficient and robust EBMs promises to significantly advance both theoretical understanding of probability modeling and practical applications across numerous domains.