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Found 4 papers, 1 papers with code
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.
Synthetic ECG Signal Generation using Probabilistic Diffusion Models
In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared.
SAD: Saliency-based Defenses Against Adversarial Examples
We measure the accuracy of our model by evaluating the effectiveness of state-of-the-art saliency methods prior to attack, under attack, and after application of cleaning methods.
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