# Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes

arXiv: https://arxiv.org/abs/2507.09362 · HTML: https://mlsanity.com/paper/2507.09362/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 12 Jul 2025.

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

- [Neural Program Repair with Execution-based Backpropagation](https://mlsanity.com/paper/2105.04123/) (2021)
- [Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders](https://mlsanity.com/paper/2008.01214/) (2020)
- [Evolution is Driven by Natural Autoencoding: Reframing Species, Interaction Codes, Cooperation, and Sexual Reproduction](https://mlsanity.com/paper/2203.11891/) (2022)

## Abstract (arXiv)

An autoencoder (AE) is a neural network that, using self-supervised training, learns a succinct parameterized representation, and a corresponding encoding and decoding process, for all instances in a given class. Here, we introduce the concept of a meta-autoencoder (MAE): an AE for a collection of autoencoders. Given a family of classes that differ from each other by the values of some parameters, and a trained AE for each class, an MAE for the family is a neural net that has learned a compact representation and associated encoder and decoder for the class-specific AEs. One application of this general concept is in research and modeling of natural evolution -- capturing the defining and the distinguishing properties across multiple species that are dynamically evolving from each other and from common ancestors. In this interim report we provide a constructive definition of MAEs, initial examples, and the motivating research directions in machine learning and biology.