Expanding the capabilities of normalizing flows in deep generative models and variational inference
<p>Deep generative models and variational Bayesian inference are two frameworks for reasoning about observed high-dimensional data, which may even be combined. A fundamental requirement of either approach is the parametrization of an expressive family of density models. Normalizing flows, some...
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| Format: | Thesis |
| Language: | English |
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2021
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