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cs.LG2025
Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions
Surojit Saha, Sarang Joshi, Ross Whitaker
Deep latent variable models (DLVMs) are designed to learn meaningful representations in an unsupervised manner, such that the hidden explanatory factors are interpretable by indepe…
cs.LG2025
ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders
Surojit Saha, Sarang Joshi, Ross Whitaker
The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimiz…
cs.LG2023
Matching aggregate posteriors in the variational autoencoder
Surojit Saha, Sarang Joshi, Ross Whitaker
The variational autoencoder (VAE) is a well-studied, deep, latent-variable model (DLVM) that efficiently optimizes the variational lower bound of the log marginal data likelihood a…