Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"

Code accompanying NCA paper titled "Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders"

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

Exploring N-dimensional latent spaces generated by neural variational autoencoders