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Amari's long-overlooked Bayesian duality and standard convex duality are fundamentally two sides of the same coin, unlocking a unified geometric foundation for probabilistic update rules in AI.
EVON transforms structured weight uncertainty into a practical tool, yielding superior performance in language model pretraining without the overhead of complex implementations.
Data-similarity can reliably guide output tracing, but its accuracy skyrockets when refined with data-influence, revealing a powerful synergy between the two measures.
Merging past knowledge with fast adaptation in the CoVON optimizer leads to superior performance in continual learning tasks, outperforming traditional methods.
