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- W4287864421 abstract "Given an inverse problem with a normalizing flow prior, we wish to estimate the distribution of the underlying signal conditioned on the observations. We approach this problem as a task of conditional inference on the pre-trained unconditional flow model. We first establish that this is computationally hard for a large class of flow models. Motivated by this, we propose a framework for approximate inference that estimates the target conditional as a composition of two flow models. This formulation leads to a stable variational inference training procedure that avoids adversarial training. Our method is evaluated on a variety of inverse problems and is shown to produce high-quality samples with uncertainty quantification. We further demonstrate that our approach can be amortized for zero-shot inference." @default.
- W4287864421 created "2022-07-26" @default.
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- W4287864421 date "2020-02-26" @default.
- W4287864421 modified "2023-09-30" @default.
- W4287864421 title "Composing Normalizing Flows for Inverse Problems" @default.
- W4287864421 doi "https://doi.org/10.48550/arxiv.2002.11743" @default.
- W4287864421 hasPublicationYear "2020" @default.
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