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This paper investigates how correlated noise, inspired by neural variability in the brain, can enhance the robustness of artificial neural networks against adversarial attacks and naturalistic image modifications.
This paper establishes the first population risk bounds for Kolmogorov-Arnold Networks trained with mini-batch SGD and DP-SGD using correlated noise, advancing theoretical understanding of KANs in privacy-sensitive domains.