1 code implementation • 25 Nov 2021 • Bernardo Aquino, Arash Rahnama, Peter Seiler, Lizhen Lin, Vijay Gupta
Adversarial examples can easily degrade the classification performance in neural networks.
no code implementations • 10 Nov 2021 • Can Karakus, Rahul Huilgol, Fei Wu, Anirudh Subramanian, Cade Daniel, Derya Cavdar, Teng Xu, Haohan Chen, Arash Rahnama, Luis Quintela
In contrast to existing solutions, the implementation of the SageMaker library is much more generic and flexible, in that it can automatically partition and run pipeline parallelism over arbitrary model architectures with minimal code change, and also offers a general and extensible framework for tensor parallelism, which supports a wider range of use cases, and is modular enough to be easily applied to new training scripts.
2 code implementations • 20 May 2020 • Arash Rahnama, Andrew Tseng
In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning.
1 code implementation • CVPR 2020 • Arash Rahnama, Andre T. Nguyen, Edward Raff
We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally.
no code implementations • 17 Jul 2019 • 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.
1 code implementation • 12 Apr 2018 • Khalique Newaz, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, Tijana Milenkovic
Experimental determination of protein function is resource-consuming.
no code implementations • 28 Sep 2017 • Arash Rahnama, Panos J. Antsaklis
We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization.
no code implementations • 13 Aug 2017 • Arash Rahnama, Abdullah Alchihabi, Vijay Gupta, Panos Antsaklis, Fatos T. Yarman Vural
We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.