1 citations · 1 across the 3 of their papers we have counts for
3 papers
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…
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
Siddharth Mansingh, Michal Kucer, Garrett Kenyon +2
Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans. Adversarial training, a process where adversarial examples are added to…
Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
Haoruo Zhao, Hassan Hijazi, Haydn Jones +3
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their d…