Speakers
Benjamin Wandelt
(Institut d'Astrophysique de Paris / The Flatiron Institute)
Lucas Makinen
(Imperial College London)
Description
Data compression to informative summaries is essential for modern data analysis. Neural regression is a popular simulation-based technique for mapping data to parameters as summaries over a prior, but is usually agnostic to how uncertainties in information geometry, or data-summary relationship, changes over parameter space. We present Fishnets, a general simulation-based, neural compression approach to calculating the Fisher information and score for arbitrary data structures as functions of parameters. These compression networks can be scaled information-optimally to arbitrary data structures, and are robust to changes in data distribution, making them ideal tools for cosmological and graph dataset analyses.
Author
Lucas Makinen
(Imperial College London)
Co-authors
Benjamin Wandelt
(Institut d'Astrophysique de Paris / The Flatiron Institute)
Dr
Justin Alsing
(Stockholm University)