Energy-Based Models: One Lens for Machine Learning

Energy-based models replace input-to-output functions with a scalar energy that scores how compatible a configuration is, the framework Yann LeCun has advocated for decades. This cursus builds it: the energy landscape and inference as finding the lowest-energy answer, how to train one when the partition function is intractable (contrastive divergence, noise-contrastive estimation, score matching, and regularized methods), and the unifying view in which classification, self-supervised learning, JEPA, and diffusion all become one idea.