2 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Adversarial examples for neural network image classifiers are known to be transferable: examples optimized to be misclassified by a source classifier are often misclassified as wel…
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Neural networks trained on visual data are well-known to be vulnerable to often imperceptible adversarial perturbations. The reasons for this vulnerability are still being debated…
It's Hard for Neural Networks To Learn the Game of Life
Jacob M. Springer, Garrett T. Kenyon
Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however…
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples
Jacob M. Springer, Charles S. Strauss, Austin M. Thresher +2
Although deep learning has shown great success in recent years, researchers have discovered a critical flaw where small, imperceptible changes in the input to the system can drasti…