Artificial Intelligence #bayesian#3d
Bayesian 3D Steerable CNNs Combine Equivariance and Uncertainty Quantification
A research paper proposes a Bayesian Steerable-CNN that simultaneously preserves SE(3)-equivariance and enables uncertainty quantification. The model achieves an expected calibration error of 0.0263 and outperforms its deterministic counterpart by up to 6.17% under distributional shift. The framework decomposes uncertainty into epistemic and aleatoric components, with a statistically significant negative correlation between epistemic uncertainty and prediction error.
Jun 16, 2026 1 source