433 citations · 1.3k across the 96 of their papers we have counts for
6 papers · 1 filter
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha +3
Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfe…
Six Potential Game-Changers in Cyber Security: Towards Priorities in Cyber Science and Engineering
Alexander Kott, Ananthram Swami, Patrick McDaniel
The fields of study encompassed by cyber science and engineering are broad and poorly defined at this time. As national governments and research communities increase their recognit…
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu +2
Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other ma…
Node Dominance: Revealing Community and Core-Periphery Structure in Social Networks
Jennifer Gamble, Harish Chintakunta, Adam Wilkerson +2
This study relates the local property of node dominance to local and global properties of a network. Iterative removal of dominated nodes yields a distributed algorithm for computi…
Network Capability in Localizing Node Failures via End-to-end Path Measurements
Liang Ma, Ting He, Ananthram Swami +2
We investigate the capability of localizing node failures in communication networks from binary states (normal/failed) of end-to-end paths. Given a set of nodes of interest, unique…
Information Cascades in Feed-based Networks of Users with Limited Attention
Sameet Sreenivasan, Kevin S. Chan, Ananthram Swami +2
We build a model of information cascades on feed-based networks, taking into account the finite attention span of users, message generation rates and message forwarding rates. Usin…