Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Deep neural networks (DNN) have achieved unprecedented success in numerous machine
learning tasks in various domains. However, the existence of adversarial examples raises …

In the past decade the mathematical theory of machine learning has lagged far behind the
triumphs of deep neural networks on practical challenges. However, the gap between theory …

Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …

We show how to turn any classifier that classifies well under Gaussian noise into a new
classifier that is certifiably robust to adversarial perturbations under the L2 norm. While this" …

As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …

We show that there may exist an inherent tension between the goal of adversarial
robustness and that of standard generalization. Specifically, training robust models may not …

We study the resilience to Byzantine failures of distributed implementations of Stochastic
Gradient Descent (SGD). So far, distributed machine learning frameworks have largely …

This paper investigates strategies that defend against adversarial-example attacks on image-
classification systems by transforming the inputs before feeding them to the system …

Abstract Machine learning models are often susceptible to adversarial perturbations of their
inputs. Even small perturbations can cause state-of-the-art classifiers with high" standard" …