In conjugate BLR, MFVI overestimates expected predictive variance on in-distribution points relative to the exact posterior, with overestimation aligned to training data directions.
Variati onal Learning is Effective for Large Deep Networks
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Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
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Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
In conjugate BLR, MFVI overestimates expected predictive variance on in-distribution points relative to the exact posterior, with overestimation aligned to training data directions.
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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.