SPIN Unprocessed October 28, 2026 ai_technology research
Lower expected Fisher rank implies better PAC generalization
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arXiv:2610.33208v1 Announce Type: new Abstract: Deep learning architectures tend to generalize well to unseen data, but explaining this capability is still an open area of research. Under the assumption of i.i.d. data, PAC-Bayes provides a bound on generalization error, and its tightness relies on a parameter distribution that covers a large volume of low-loss solutions. The Fisher information metric endows the parameter space with a geometry for which distances reflect functional changes, and w
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