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Energy-based model
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<p>An energy-based model (EBM) (also called Canonical Ensemble Learning or Learning via Canonical Ensemble – CEL and LCE, respectively) is an application of <a href="/facts/Canonical_ensemble/GIhyzkb8">canonical ensemble</a> formulation from <a href="/facts/Statistical_physics/SnlJE78a">statistical physics</a> for <a href="/facts/Machine_learning/e0w0XJTu">learning from data</a>. The approach prominently appears in <a href="/facts/Generative_artificial_intelligence/ykT3GGyT">generative artificial intelligence</a>. </p><p>EBMs provide a unified framework for many probabilistic and non-probabilistic approaches to such learning, particularly for training <a href="/facts/Graphical_model/XxfmKhmM">graphical</a> and other structured models. </p><p>An EBM learns the characteristics of a target <a href="/facts/Dataset/eDvegUTK">dataset</a> and generates a similar but larger dataset. EBMs detect the <a href="/facts/Latent_variables/ohg99BnW">latent variables</a> of a dataset and generate new datasets with a similar distribution. </p><p>Energy-based generative neural networks is a class of <a href="/facts/Generative_model/JUgExNIP">generative models</a>, which aim to learn explicit probability distributions of data in the form of energy-based models, the energy functions of which are parameterized by modern deep <a href="/facts/Artificial_neural_network/6V1jMlkx">neural networks</a>. </p><p><a href="/facts/Boltzmann_machine/2wyLI0pI">Boltzmann machines</a> are a special form of energy-based models with a specific parametrization of the energy. </p>