אוטואנקודרים סימטריים עמוקים

תקציר מקורי באנגליתarXiv:2506.11641v2 Announce Type: replace-cross Abstract: Deep autoencoders have become a fundamental tool in various machine learning applications, ranging from dimensionality reduction and reduced order modeling of partial differential equations to anomaly detection and neural machine translation. Despite their empirical success, a solid theoretical foundation for their expressiveness remains elusive, particularly when compared to classical projection-based techniques. In this work, we aim to take a step forward in this direction by presenting a comprehensive analysis of what we refer to as symmetric autoencoders, a broad class of deep learning architectures that have surfaced frequently in the recent literature. Specifically, we introduce a formal distinction between different classes o