AI RESEARCH
Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoders
arXiv CS.AI
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ArXi:2404.15390v3 Announce Type: replace-cross Optimal computations under uncertainty require an adequate probabilistic representation about beliefs. Deep generative models, and specifically Variational Autoencoders (VAEs), have the potential to meet this demand by building latent representations that learn to associate uncertainties with inferences while avoiding their characteristic intractable computations.