Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using ...

Abstract: Leveraging the fact that speaker identity and content vary on different time scales, factorized hierarchical variational autoencoder (FHVAE) uses different latent variables to symbolize ...

This code provides a python implementation of the Variational Latent Mode Decomposition (VLMD) algorithm [1] for extracting modes and associated connectivity structures from multivariate signals. VLMD ...

A denoising autoencoder tailored for quantitative-finance data. It learns to strip away market “noise” from raw price/volume series and surfaces a compact latent representation that can be piped into ...