Summary
This paper introduces Joint Embedding Variational Bayes (TMLR ’26), a new approach to integrate operational variational semantics into joint-embedding architectures for non-contrastive representation learning. It achieves this by making three coupled choices, notably factorizing the embedding likelihood into directional and radial terms to model angular alignment and representation norm separately. While mathematically dense, the core concept aims to enhance understanding and application of these learning models.
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