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  Bayesian reasoning with trained neural networks

Knollmüller, J., & Ensslin, T. A. (2021). Bayesian reasoning with trained neural networks. Entropy, 23(6): 693. doi:10.3390/e23060693.

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Knollmüller, Jakob1, Author              
Ensslin, Torsten A.1, Author              
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1Computational Structure Formation, MPI for Astrophysics, Max Planck Society, ou_2205642              

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 Abstract: We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The tasks at hand were formulated as Bayesian inference problems, which we approximately solved through variational or sampling techniques. The approach built on top of already trained networks, and the addressable questions grew super-exponentially with the number of available networks. In its simplest form, the approach yielded conditional generative models. However, multiple simultaneous constraints constitute elaborate questions. We compared the approach to specifically trained generators, showed how to solve riddles, and demonstrated its compatibility with state-of-the-art architectures.

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Language(s): eng - English
 Dates: 2021-05-31
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/e23060693
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Title: Entropy
Source Genre: Journal
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Publ. Info: Basel : Molecular Diversity Preservation International
Pages: - Volume / Issue: 23 (6) Sequence Number: 693 Start / End Page: - Identifier: ISSN: 1099-4300
CoNE: https://pure.mpg.de/cone/journals/resource/110978984445793