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SpiKFAX is proposed, a second-order optimization method that formulates a computationally tractable, Kronecker-factored approximation of the Fisher information matrix specifically adapted to the structure of SNNs.
This work investigates the effect of BN layers on the memorization of atypical or outlier samples and its implications for privacy leakage, and provides both practical and theoretical insights into how normalization layers can amplify the influence of rare or sensitive training examples.
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