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Showing 1 to 7 of 7 for “"Projected gradient descent"”.
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A non-convex framework for structured non-stationary covariance recovery theory and application
… combined with iteratively re ned alternating projected gradient descent. We prove a linear convergence rate for the proposed descent scheme and establish sample complexity guarantees for the estimator. As a motivating example, we consider the neuroscience application of estimation of dynamic …
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Dense subgraph detection on multi-layered networks
… optimization-based formulation, we develop the projected gradient descent algorithms that bear the following distinctive advantages. First (applicability), the model is suitable for the generally defined multi-layered networks without requirements for sharing the same set of nodes across layers …
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Finite Gaussian Neurons: Defending Against Adversarial Attacks by Making Neural Networks Say "I Don’t Know"
… in their predictions over randomized and Fast Gradient Sign Method adversarial images when compared to classical neural networks, while maintaining high accuracy and confidence over real MNIST images. <br />To further validate the capacity of Finite Gaussian Neurons to protect from adversarial …
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An evaluation of the robustness of the natural-adversarial mutual information-based defense and malware classification against adversarial attacks for deep learning
… We will compare MI-Craft to standard projected gradient descent for the creation of adversarial examples, as well as demonstrate the effectiveness of MI-Craft and NAMID under the CIFAR10 and MalImg datasets.
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Generative models and robustness in deep learning for inverse problems
… prior to solve linear inverse problems using projected gradient descent (PGD). Experiments show that our approach provides a speed-up of 60-80× over earlier GAN-based recovery methods along with better accuracy. Our main theoretical result is that if the measurement matrix is moderately …
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Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery
… (which we show is equivalent to a sparsity-projected gradient descent). Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main …
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Secure Machine Learning Based RF Signal Classification for Wireless Systems
… three types of low-power AML perturbations: Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool, while varying the amount of information available to the attacker. On one extreme (so-called ``white-box" attack), the attacker has complete knowledge of the defender's …