Adversarial KA
Regarding the representation theorem of Kolmogorov and Arnold (KA) as an algorithm for representing or {\guillemotleft}expressing{\guillemotright} functions, we test its robustness by analyzing its ability to withstand adversarial attacks. We find KA to be robust to countable collections of continuous adversaries, but unearth a question about the equi-continuity of the outer functions that, so far, obstructs taking limits and defeating continuous groups of adversaries. This question on the regularity of the outer functions is relevant to the debate over the applicability of KA to the general theory of NNs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
Adversarial examples provoke weak reliability and potential security issues in deep neural networks. Although adversarial training has been widely studied to improve adversarial robustness, it works in an over-parameteri…
Adversarial RobustnessModel CompressionAdversarial Consistency and the Uniqueness of the Adversarial Bayes Classifier
Minimizing an adversarial surrogate risk is a common technique for learning robust classifiers. Prior work showed that convex surrogate losses are not statistically consistent in the adversarial context -- or in other wo…
ClassificationGradient-Free Adversarial Purification with Diffusion Models
Adversarial training and adversarial purification are two effective and practical defense methods to enhance a model's robustness against adversarial attacks. However, adversarial training necessitates additional trainin…
Adversarial DefenseAdversarial PurificationSuper-ResolutionUnderstanding Adversarial Robustness Against On-manifold Adversarial Examples
Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One of the hypotheses of the existence of th…
Adversarial RobustnessAdversarial Training against Location-Optimized Adversarial Patches
Deep neural networks have been shown to be susceptible to adversarial examples -- small, imperceptible changes constructed to cause mis-classification in otherwise highly accurate image classifiers. As a practical altern…