6 citations · 12 across the 4 of their papers we have counts for
5 papers
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression
Emilio Rafael Balda, Arash Behboodi, Niklas Koep +1
Neural networks have been shown to be vulnerable against minor adversarial perturbations of their inputs, especially for high dimensional data under attacks. To comba…
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks
Peter Langenberg, Emilio Rafael Balda, Arash Behboodi +1
Recently, there has been an abundance of works on designing Deep Neural Networks (DNNs) that are robust to adversarial examples. In particular, a central question is which features…
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples
Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar
Despite the tremendous success of deep neural networks in various learning problems, it has been observed that adding an intentionally designed adversarial perturbation to inputs o…
On Generation of Adversarial Examples using Convex Programming
Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar
It has been observed that deep learning architectures tend to make erroneous decisions with high reliability for particularly designed adversarial instances. In this work, we show…
First-Order Perturbation Analysis of the SECSI Framework for the Approximate CP Decomposition of 3-D Noise-Corrupted Low-Rank Tensors
Sher Ali Cheema, Emilio Rafael Balda, Yao Cheng +3
The Semi-Algebraic framework for the approximate Canonical Polyadic (CP) decomposition via SImultaneaous matrix diagonalization (SECSI) is an efficient tool for the computation of…