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Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e.g., instrumentation errors, or added random noise.
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 1906
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Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 1906
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Lectures on the coupling method
T. Lindvall · 2002
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Vladimir Igorevich Bogachev, Aleksandr Viktorovich Kolesnikov, and Kirill Vladimirovich Medvedev · 2005
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Cédric Villani · 2009
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
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Sum-of-squares polynomial flow
Priyank Jaini, Kira A Selby, and Yaoliang Yu · 2019
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Waveglow: A flow-based generative network for speech synthesis
Ryan Prenger, Rafael Valle, and Bryan Catanzaro · 2019
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Improving exploration in soft-actor-critic with normalizing flows policies
Patrick Nadeem Ward, Ariella Smofsky, and Avishek Joey Bose · 2019
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Bernstein Operators and Their Properties
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Normalizing flows: An introduction and review of current methods
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Targeted free energy estimation via learned mappings
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