Re-Envisioning Numerical Information Field Theory (NIFTyre): A Library for Gaussian Processes and Variational Inference
<|reference_start|>Re-Envisioning Numerical Information Field Theory (NIFTyre): A Library for Gaussian Processes and Variational Inference: 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.<|reference_end|>
@article{edenhofer2024re-envisioning,
    title={Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library
  for Gaussian Processes and Variational Inference},
    author={Gordian Edenhofer, Philipp Frank, Jakob Roth, Reimar H. Leike, Massin
  Guerdi, Lukas I. Scheel-Platz, Matteo Guardiani, Vincent Eberle, Margret
  Westerkamp, and Torsten A. En{ss}lin},
    journal={Journal of Open Source Software, volume 9(98), year 2024, page
  6593},
    year={2024},
    doi={10.21105/joss.06593},
    archivePrefix={arXiv},
    eprint={2402.16683},
    primaryClass={astro-ph.IM cs.LG stat.ML}
}