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Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications)

Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications)

Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data
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Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications) Other -

by Paul Lyonel Hagemann; Johannes Hertrich; Gabriele Steidl

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  • Title Generalized Normalizing Flows via Markov Chains (Elements in Non-local Data Interactions: Foundations and Applications)
  • Author Paul Lyonel Hagemann; Johannes Hertrich; Gabriele Steidl
  • Binding Other
  • Condition New
  • Pages 66
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date
  • Features Bibliography
  • Bookseller's Inventory # 6395933727
  • ISBN 9781009331005 / 1009331000
  • Weight 0.22 lbs (0.10 kg)
  • Dimensions 9 x 6 x 0.14 in (22.86 x 15.24 x 0.36 cm)
  • Category Computers - General Information
  • Library of Congress subjects Markov processes
  • Library of Congress Catalogue Number 2023000475
  • Dewey Decimal Code 519.233
  • Quantity available 4

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From the publisher

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.

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