What is recorded here?
Kingma and Welling used reparameterization to make variational learning in continuous latent-variable models amenable to stochastic gradient optimization.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Optimization uses a variational lower bound and an approximate posterior. This is not exact inference for every latent-variable model. The pinned v11 is a later revision of a work first posted in 2013.
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People and systems
Diederik P Kingma; Max Welling
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Documented countries / regions: Netherlands
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English catalogue narrative
Variational autoencoders through reparameterized inference
Kingma and Welling used reparameterization to make variational learning in continuous latent-variable models amenable to stochastic gradient optimization.
Scientific scope
Optimization uses a variational lower bound and an approximate posterior. This is not exact inference for every latent-variable model. The pinned v11 is a later revision of a work first posted in 2013.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Diederik P Kingma; Max Welling
Source paper byline retained, including repeated label positions. Names are not verified unique identities or measurements of individual effort.