2025/07/12 by Assaf Marron, Smadar Szekely, Marron, Assaf +5
Computer Science · #Evolutionary Algorithms and Applications #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.2507.09362
openalex publication_date 2025/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.