ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation

Variational autoencoders generate samples from probabilistic latent representations but do not distinguish uncertainty about the latent location from variability around it. We formulate ELVAE, an evidential learning-based VAE in which each latent coordinate is governed by an input-dependent normal-inverse-gamma posterior. This hierarchy yields an explicit latent-location uncertainty that can be used during generation, not merely reported after inference: low-uncertainty anchors support more reli

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ethics.ai (11 August 2026), “ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation,” evidence record 18431, https://ethics.ai/record/18431 (originally published by arXiv).

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