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Persistent Classification: A New Approach to Stability of Data and Adversarial Examples
This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary.
An Exact Kernel Equivalence for Finite Classification Models
We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gradient descent as a kernel machine.
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