Energy Based Model

Energy-based models (EBMs) are probabilistic models that define a probability distribution through an energy function, aiming to learn complex data distributions and generate new samples. Current research focuses on improving training efficiency and sample quality, often employing techniques like contrastive divergence, diffusion processes, and Markov Chain Monte Carlo (MCMC) methods, with applications of EBMs ranging from image generation and anomaly detection to speech synthesis and robotics. The ability of EBMs to model complex, high-dimensional data and their connection to statistical physics makes them a significant area of research with broad implications across various scientific fields and practical applications.