Marginalizing Flows
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2020-07-30
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en
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Abstract
We introduce marginalizing
ows, an extension to normalizing
ows which allow
for better density estimation by marginaliziation of auxiliary random variables.
We give an outline of the shortcomings of normalizing
ows and motivate our
approach. We trained models with an architecture based on Real NVP by Dinh
et al.[5] on several datasets. For low-dimensional data we see that marginalizing
ows consistently predict higher likelihood than normalizing
ows, but our results
do not generalize to higher-dimensional data like images.
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Faculteit der Sociale Wetenschappen