
Compression of volumesurface integral equation matrices via Tucker decomposition for magnetic resonance applications
In this work, we propose a method for the compression of the coupling ma...
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Fast Training of Provably Robust Neural Networks by SingleProp
Recent works have developed several methods of defending neural networks...
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HigherOrder Certification for Randomized Smoothing
Randomized smoothing is a recently proposed defense against adversarial ...
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Robust Deep Reinforcement Learning through Adversarial Loss
Deep neural networks, including reinforcement learning agents, have been...
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Proper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial example...
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Rethinking Randomized Smoothing for Adversarial Robustness
The fragility of modern machine learning models has drawn a considerable...
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Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model i...
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Fastened CROWN: Tightened Neural Network Robustness Certificates
The rapid growth of deep learning applications in real life is accompani...
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Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
Deep neural networks are known to be fragile to small adversarial pertur...
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POPQORN: Quantifying Robustness of Recurrent Neural Networks
The vulnerability to adversarial attacks has been a critical issue for d...
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PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing stateoftheart machine learning mo...
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CNNCert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Verifying robustness of neural network classifiers has attracted great i...
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Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying r...
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On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
CLEVER (CrossLipschitz Extreme Value for nEtwork Robustness) is an Extr...
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Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (Re...
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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
The robustness of neural networks to adversarial examples has received g...
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Computing lowrank approximations of largescale matrices with the Tensor Network randomized SVD
We propose a new algorithm for the computation of a singular value decom...
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Luca Daniel
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