  Energy-Based Models (EBMs) have emerged as a powerful framework in the realm
of generative modeling, offering a unique perspective that aligns closely with
principles of statistical mechanics. This review aims to provide physicists
with a comprehensive understanding of EBMs, delineating their connection to
other generative models such as Generative Adversarial Networks (GANs),
Variational Autoencoders (VAEs), and Normalizing Flows. We explore the sampling
techniques crucial for EBMs, including Markov Chain Monte Carlo (MCMC) methods,
and draw parallels between EBM concepts and statistical mechanics, highlighting
the significance of energy functions and partition functions. Furthermore, we
delve into state-of-the-art training methodologies for EBMs, covering recent
advancements and their implications for enhanced model performance and
efficiency. This review is designed to clarify the often complex
interconnections between these models, which can be challenging due to the
diverse communities working on the topic.
