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Found 8 papers, 4 papers with code
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Adversarial examples can easily degrade the classification performance in neural networks.
Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training
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.
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning.
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally.
Connecting Lyapunov Control Theory to Adversarial Attacks
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.
Network-based protein structural classification
Experimental determination of protein function is resource-consuming.
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization.
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.
