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Masked Autoregressive Flow for Density Estimation
[article]
2018
arXiv
pre-print
Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow. This type of flow
arXiv:1705.07057v4
fatcat:svp6kx4ebjdxdpp7zhtfrlrj54