Enhancing interpretability in generative modeling: statistically disentangled latent spaces guided by generative factors in scientific datasets

• Published in: Machine learning Vol. 114; no. 9; p. 197
• Main Authors: Ganguli, Arkaprabha, Ramachandra, Nesar, Bessac, Julie, Constantinescu, Emil
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2025; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Computer Science; Control; Datasets; Independent variables; Machine Learning; Mechatronics; Natural Language Processing (NLP); Physical factors; Robotics; Simulation and Modeling; Subject specialists; Variables; Representation learning; Variational autoencoder; Disentangled generative factors; Posterior regularization

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder–decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensio...