[R] Joint Embedding Variational Bayes (TMLR ’26)

Variational Joint Embedding (VJE) is a framework that synthesizes joint embedding and variational inference to enable self-supervised learning of probabilistic representations in a reconstruction-free, non-contrastive setting. VJE maximizes a symmetric conditional evidence lower bound (ELBO) for a latent-variable model defined directly on encoder embeddings, contrasting with energy-based predictive objectives. The framework employs a heavy-tailed Student-t model with polar decomposition to prevent training instabilities and uses an amortized inference network with shared feature-wise variances to capture anisotropic uncertainty.

Source: r/MachineLearning | 2026-04-30