Viktoriia Chekalina

Scalable Kronecker-Factored Fisher Approximation for Neural Network Parameter Sensitivity

The Fisher Information Matrix (FIM) provides a principled geometric framework for parameter sensitivity in neural networks, but directly computing and using the full FIM is infeasible in high-dimensional models. As a result, most existing methods rely on diagonal approximations that discard importan…