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MFVI can paradoxically overestimate predictive variance, leading to predictions that exceed those of the exact posterior for in-distribution test points.
Merging past knowledge with fast adaptation in the CoVON optimizer leads to superior performance in continual learning tasks, outperforming traditional methods.
