Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …

Torchattacks : A Pytorch Repository for Adversarial Attacks Page 1 arXiv:2010.01950v3 [cs.LG]
19 Feb 2021 Torchattacks: A PyTorch Repository for Adversarial Attacks Hoki Kim Seoul …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Deep neural networks are vulnerable to adversarial examples, which becomes one of the
most important research problems in the development of deep learning. While a lot of efforts …

The point estimates of ReLU classification networks—arguably the most widely used neural
network architecture—have been shown to yield arbitrarily high confidence far away from …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …