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Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation.
HINT: Hierarchical Invertible Neural Transport for general and sequential Bayesian inference
Detommaso, G.; Kruse, J.; Ardizzone, L.; Rother, C.; Köthe, U.; and Scheichl, R. 2019 · 1905
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Guided image generation with conditional invertible neural networks
Ardizzone, L.; Lüth, C.; Kruse, J.; Rother, C.; and Köthe, U. 2019b · 1907
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Normalizing flows: An introduction and review of current methods
Kobyzev, I.; Prince, S.; and Brubaker, M. A. 2019 · 1908
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G.; Nalisnick, E.; Rezende, D. J.; Mohamed, S.; and Lakshminarayanan, B. 2019 · 1912
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Learning likelihoods with conditional normalizing flows
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