Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes
Abstract
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.
Cite
@article{arxiv.2507.09362,
title = {Meta-autoencoders: An approach to discovery and representation of relationships between dynamically evolving classes},
author = {Assaf Marron and Smadar Szekely and Irun Cohen and David Harel},
journal= {arXiv preprint arXiv:2507.09362},
year = {2025}
}