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11/25/2021
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Adversarial examples can easily degrade the classification performance i...
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11/10/2021
Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training
With deep learning models rapidly growing in size, systems-level solutio...
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05/20/2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
Machine learning models have been successfully applied to a wide range o...
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11/12/2019
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Deep neural networks (DNNs) are vulnerable to subtle adversarial perturb...
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07/17/2019
Connecting Lyapunov Control Theory to Adversarial Attacks
Significant work is being done to develop the math and tools necessary t...
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04/12/2018
Network-based protein structural classification
Experimental determination of protein function is resource-consuming. As...
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09/28/2017
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
In this paper, we show synchronization for a group of output passive age...
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08/13/2017