8 citations · 19 across the 9 of their papers we have counts for
4 papers · 1 filter
Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training
Can Karakus, Rahul Huilgol, Fei Wu +7
With deep learning models rapidly growing in size, systems-level solutions for large-model training are required. We present Amazon SageMaker model parallelism, a software library…
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Bernardo Aquino, Arash Rahnama, Peter Seiler +2
Adversarial examples can easily degrade the classification performance in neural networks. Empirical methods for promoting robustness to such examples have been proposed, but often…
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…