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.
Primary author
Lucas Makinen
(Imperial College London)
Co-authors
Benjamin Wandelt
(Institut d'Astrophysique de Paris / The Flatiron Institute)
Dr
Justin Alsing
(Stockholm University)
