About: Flow-based generative model

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A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one. The direct modeling of likelihood provides many advantages. For example, the negative log-likelihood can be directly computed and minimized as the loss function. Additionally, novel samples can be generated by sampling from the initial distribution, and applying the flow transformation.

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rdfs:label	Flow-based generative model (en)
rdfs:comment	A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one. The direct modeling of likelihood provides many advantages. For example, the negative log-likelihood can be directly computed and minimized as the loss function. Additionally, novel samples can be generated by sampling from the initial distribution, and applying the flow transformation. (en)
dbp:wikiPageUsesTemplate	dbt:Reflist dbt:Machine_learning_bar
Subject	Statistical models Machine learning Probabilistic models
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Flow-based_generative_model?oldid=1021125839&ns=0
Wikipage page ID	67064736 (xsd:integer)
page length (characters) of wiki page	7915 (xsd:nonNegativeInteger)
Wikipage revision ID	1021125839 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Probability density function Probability distribution Autoencoder Statistical models Generative adversarial network Generative model Jacobian matrix and determinant Machine learning Probabilistic models Deep learning Determinant Inverse function Inverse function theorem Invertible matrix Loss function Mathematical induction Random variable
has abstract	A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one. The direct modeling of likelihood provides many advantages. For example, the negative log-likelihood can be directly computed and minimized as the loss function. Additionally, novel samples can be generated by sampling from the initial distribution, and applying the flow transformation. In contrast, many alternative generative modeling methods such as variational autoencoder (VAE) and generative adversarial network do not explicitly represent the likelihood function. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Flow-based_generative_model
is Link from a Wikipage to another Wikipage of	Generative adversarial network Generative model
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Flow-based_generative_model

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