# Autoencoding Labeled Interpolator, Inferring Parameters From Image, And Image From Parameters

arXiv: https://arxiv.org/abs/2312.04640 · HTML: https://mlsanity.com/paper/2312.04640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Opening the Black Box of Deep Neural Networks via Information](https://mlsanity.com/paper/1703.00810/) (2017)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction](https://mlsanity.com/paper/2201.08506/) (2022)

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

- [Validation and Calibration of Semi-Analytical Models for the Event Horizon Telescope Observations of Sagittarius A*](https://mlsanity.com/paper/2504.18624/) (2025)

## Abstract (arXiv)

The Event Horizon Telescope (EHT) provides an avenue to study black hole accretion flows on event-horizon scales. Fitting a semi-analytical model to EHT observations requires the construction of synthetic images, which is computationally expensive. This study presents an image generation tool in the form of a generative machine learning model, which extends the capabilities of a variational autoencoder. This tool can rapidly and continuously interpolate between a training set of images and can retrieve the defining parameters of those images. Trained on a set of synthetic black hole images, our tool showcases success in both interpolating black hole images and their associated physical parameters. By reducing the computational cost of generating an image, this tool facilitates parameter estimation and model validation for observations of black hole system.