Energy-based model

An energy-based model (EBM) (Canonical Ensemble Learning(CEL) or Learning via Canonical Ensemble (LCE)) is an application of canonical ensemble formulation of statistical physics for learning from data problems. Approach prominently appears in generative models.

GMs learn an underlying data distribution by analyzing a sample dataset. Once trained, a GM can produce other datasets that also match the data distribution. EBMs provide a unified framework for many probabilistic and non-probabilistic approaches to such learning, particularly for training graphical and other structured models.

An EBM learns the characteristics of a target dataset and generates a similar but larger dataset. EBMs detect the latent variables of a dataset and generate new datasets with a similar distribution.

Energy-based generative neural networks is a class of generative models, which aim to learn explicit probability distributions of data in the form of energy-based models whose energy functions are parameterized by modern deep neural networks.

Boltzmann machines are a special form of energy-based models with a specific parametrization of the energy.