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cs.LG2024
A Geometric Framework for Adversarial Vulnerability in Machine Learning
Brian Bell
This work starts with the intention of using mathematics to understand the intriguing vulnerability observed by ~\citet{szegedy2013} within artificial neural networks. Along the wa…
cs.LG2024
Persistent Classification: A New Approach to Stability of Data and Adversarial Examples
Brian Bell, Michael Geyer, David Glickenstein +4
There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high-dimensionality of the data, high codimension i…
cs.LG2023★ 1 cited
An Exact Kernel Equivalence for Finite Classification Models
Brian Bell, Michael Geyer, David Glickenstein +2
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 gra…