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Found 4 papers, 0 papers with code
Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees
We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns.
Subspace based low rank and joint sparse matrix recovery
We consider the recovery of a low rank and jointly sparse matrix from under sampled measurements of its columns.
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
Trust, but Verify: Robust Image Segmentation using Deep Learning
We describe a method for verifying the output of a deep neural network for medical image segmentation that is robust to several classes of random as well as worst-case perturbations i. e. adversarial attacks.
