AWhat changed
Kingma and Welling used reparameterization to make variational learning in continuous latent-variable models amenable to stochastic gradient optimization.
Scientific scope
Kingma and Welling used reparameterization to make variational learning in continuous latent-variable models amenable to stochastic gradient optimization. Scope limitation: 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.
Evidence
Assessment uses the catalogue-linked source evidence for the stated contribution. 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.
Contributors
- Diederik P Kingma · SCIENCE: Named source contributor; exact labor shares are not measured.
- Max Welling · SCIENCE: Named source contributor; exact labor shares are not measured.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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