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Optimizing Constraint Selection In A Design Verification Environment For Efficient Coverage Closure, Vanessa Cooper
Optimizing Constraint Selection In A Design Verification Environment For Efficient Coverage Closure, Vanessa Cooper
CCE Theses and Dissertations
No abstract provided.
Adversarial Training Of Deep Neural Networks, Anabetsy Termini
Adversarial Training Of Deep Neural Networks, Anabetsy Termini
CCE Theses and Dissertations
Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …