2 citations · 3 across the 4 of their papers we have counts for
6 papers
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
Arash Rahnama, Andrew Tseng
Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful…
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Arash Rahnama, Andre T. Nguyen, Edward Raff
Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the D…
Connecting Lyapunov Control Theory to Adversarial Attacks
Arash Rahnama, Andre T. Nguyen, Edward Raff
Significant work is being done to develop the math and tools necessary to build provable defenses, or at least bounds, against adversarial attacks of neural networks. In this work,…
Network-based protein structural classification
Khalique Newaz, Mahboobeh Ghalehnovi, Arash Rahnama +2
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, prote…
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
Arash Rahnama, Abdullah Alchihabi, Vijay Gupta +2
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive…
QSR-Dissipativity and Passivity Analysis of Event-Triggered Networked Control Cyber-Physical Systems
Arash Rahnama, Meng Xia, Panos J. Antsaklis
Input feed-forward output feedback passive (IF-OFP) systems define a great number of dynamical systems. In this report, we show that dissipativity and passivity-based control combi…