AI RESEARCH
Entropic Auto-Encoding via Implicit Free-Energy Minimization
arXiv CS.LG
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ArXi:2605.16164v1 Announce Type: new Despite their ubiquity, variational autoencoders (VAEs) inherently suffer from posterior collapse, a failure mode in which latent variables are effectively ignored. This failure arises because explicit prior imposition drives optimization toward loss landscape regions corresponding to uninformative latent representations. Here, we