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Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications) - Softcover
- PublisherCambridge University Press
- Publication date2023
- ISBN 10 1009331000
- ISBN 13 9781009331005
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages66
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Generalized Normalizing Flows Via Markov Chains (Paperback or Softback)
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Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications)
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Generalized Normalizing Flows via Markov Chains (Paperback)
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Paperback. Condition: new. Paperback. Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors consider stochastic normalizing flows as a pair of Markov chains fulfilling some properties, and show how many state-of-the-art models for data generation fit into this framework. Indeed numerical simulations show that including stochastic layers improves the expressivity of the network and allows for generating multimodal distributions from unimodal ones. The Markov chains point of view enables the coupling of both deterministic layers as invertible neural networks and stochastic layers as Metropolis-Hasting layers, Langevin layers, variational autoencoders and diffusion normalizing flows in a mathematically sound way. The authors' framework establishes a useful mathematical tool to combine the various approaches. Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors' framework establishes a useful mathematical tool to combine the various approaches. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009331005
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Generalized Normalizing Flows via Markov Chains
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Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications)
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Generalized Normalizing Flows via Markov Chains
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Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications)
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Generalized Normalizing Flows via Markov Chains
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Generalized Normalizing Flows via Markov Chains (Paperback)
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Paperback. Condition: new. Paperback. Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors consider stochastic normalizing flows as a pair of Markov chains fulfilling some properties, and show how many state-of-the-art models for data generation fit into this framework. Indeed numerical simulations show that including stochastic layers improves the expressivity of the network and allows for generating multimodal distributions from unimodal ones. The Markov chains point of view enables the coupling of both deterministic layers as invertible neural networks and stochastic layers as Metropolis-Hasting layers, Langevin layers, variational autoencoders and diffusion normalizing flows in a mathematically sound way. The authors' framework establishes a useful mathematical tool to combine the various approaches. Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors' framework establishes a useful mathematical tool to combine the various approaches. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781009331005
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Generalized Normalizing Flows via Markov Chains (Paperback)
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ISBN 10: 1009331000
ISBN 13: 9781009331005
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Paperback. Condition: new. Paperback. Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors consider stochastic normalizing flows as a pair of Markov chains fulfilling some properties, and show how many state-of-the-art models for data generation fit into this framework. Indeed numerical simulations show that including stochastic layers improves the expressivity of the network and allows for generating multimodal distributions from unimodal ones. The Markov chains point of view enables the coupling of both deterministic layers as invertible neural networks and stochastic layers as Metropolis-Hasting layers, Langevin layers, variational autoencoders and diffusion normalizing flows in a mathematically sound way. The authors' framework establishes a useful mathematical tool to combine the various approaches. Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors' framework establishes a useful mathematical tool to combine the various approaches. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781009331005
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