Deep Autoencoders

Deep autoencoders are neural networks designed to learn compressed representations of data, primarily for dimensionality reduction and anomaly detection. Current research emphasizes theoretical underpinnings of their performance, particularly in anomaly detection where minimizing information entropy in the latent space is crucial, and in extending their application to diverse fields like reduced-order modeling of PDEs and hyperspectral unmixing. These advancements improve the robustness and interpretability of autoencoders, leading to more reliable and efficient solutions in various scientific and engineering domains, including materials science, image analysis, and signal processing.