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Re-envisioning numerical information field theory (NIFTy.re): A library for gaussian processes and variational inference.
JOSS, DOI: 10.21105/joss.06593 (2024)
maging is the process of transforming noisy, incomplete data into a space that humans
can interpret. NIFTy is a Bayesian framework for imaging and has already successfully been
applied to many fields in astrophysics. Previous design decisions held the performance and
the development of methods in NIFTy back. We present a rewrite of NIFTy, coined NIFTy.re,
which reworks the modeling principle, extends the inference strategies, and outsources much
of the heavy lifting to JAX. The rewrite dramatically accelerates models written in NIFTy, lays
the foundation for new types of inference machineries, improves maintainability, and enables
interoperability between NIFTy and the JAX machine learning ecosystem.
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Publication type
Article: Journal article
Document type
Review
Language
english
Publication Year
2024
HGF-reported in Year
2024
ISSN (print) / ISBN
2475-9066
e-ISSN
2475-9066
Publisher
Open Journals
Reviewing status
Peer reviewed
Institute(s)
Institute of Biological and Medical Imaging (IBMI)
Institute of AI for Health (AIH)
Institute of AI for Health (AIH)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-505500-001
G-540002-001
G-540002-001
Erfassungsdatum
2024-10-17