Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library
  for Gaussian Processes and Variational Inference
Gordian Edenhofer Philipp Frank Jakob Roth Reimar H. Leike Massin Guerdi Lukas I. Scheel-Platz Matteo Guardiani Vincent Eberle Margret Westerkamp Torsten A. Enßlin
Imaging 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.