5 accepted papers
This work concerns the development of deep networks that are certifiably robust to adversarial attacks. Joint robust classification-detection was recently introduced as a certified defense mechanism, where adversarial examples are either correctly classified or assigned to the “abstain” class. In th…
Off-Policy reinforcement learning has been the driving force for the state-of-the-art conversational AIs leading to more natural human-agent interactions and improving the user satisfaction for goal-oriented agents. However, in large-scale commercial settings, it is often challenging to balance betw…
Adversarial attacks against deep networks can be defended against either by building robust classifiers or, by creating classifiers that can \emph{detect} the presence of adversarial perturbations. Although it may intuitively seem easier to simply detect attacks rather than build a robust classifie…
Despite their well-documented learning capabilities in clean environments, deep convolutional neural networks (CNNs) are extremely fragile in adversarial settings, where carefully crafted perturbations created by an attacker can easily disrupt the task at hand. Numerous methods have been proposed fo…
In next generation cellular networks (5G) the access points (APs) are anticipated to be equipped with storage devices to serve locally requests for reusable popular contents by caching them at the edge of the network. The ultimate goal is to shift part of the load on the back-haul links from on-peak…