AINeutralarXiv – CS AI · Jun 46/10
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Uncertainty Estimation using Variance-Gated Distributions
Researchers propose a variance-gated framework for uncertainty quantification in neural networks that decomposes predictive uncertainty using signal-to-noise ratios rather than traditional additive methods. The approach scales predictions by confidence factors derived from ensembles and reveals potential diversity collapse in committee machines, advancing how machine learning models evaluate per-sample uncertainty for high-risk applications.
