# Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference

arXiv: https://arxiv.org/abs/2402.16683 · HTML: https://mlsanity.com/paper/2402.16683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Efficient and Modular Implicit Differentiation](https://mlsanity.com/paper/2105.15183/) (2021)
- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Equinox: neural networks in JAX via callable PyTrees and filtered transformations](https://mlsanity.com/paper/2111.00254/) (2021)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Metric Gaussian Variational Inference](https://mlsanity.com/paper/1901.11033/) (2019)
- [Geometric variational inference](https://mlsanity.com/paper/2105.10470/) (2021)
- [The Galactic 3D large-scale dust distribution via Gaussian process regression on spherical coordinates](https://mlsanity.com/paper/2204.11715/) (2022)
- [Field dynamics inference via spectral density estimation](https://mlsanity.com/paper/1708.05250/) (2017)
- [Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)](https://mlsanity.com/paper/2206.10634/) (2022)

## Built on by (most cited first, 1 of 1)

- [JAXbind: Bind any function to JAX](https://mlsanity.com/paper/2403.08847/) (2024)

## Abstract (arXiv)

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.