Hidden Axis of Uncertainty: Latent-Posterior Alignment in Graph Neural Networks with Bayesian Output Layers
Read the original on arXiv Machine Learning →The paper introduces Latent-Posterior Alignment (LPA), a phenomenon where predictive uncertainty in Graph Neural Networks with Bayesian output layers decreases as latent representations align with lower‑variance posterior directions, even without posterior variance contraction. Through interventional experiments, the authors demonstrate LPA’s functional role in shaping uncertainty and propose Alignment‑Guided Learning (AGL) to explicitly promote this alignment during training. AGL reduces predictive uncertainty, preserves accuracy, and improves structural calibration, ensuring model confidence reflects data density.
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