433 citations · 1.3k across the 96 of their papers we have counts for
7 papers · 1 filter
Outlier Detection from Network Data with Subnetwork Interpretation
Xuan-Hong Dang, Arlei Silva, Ambuj Singh +2
Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computi…
Modeling Group Dynamics Using Probabilistic Tensor Decompositions
Lin Li, Ananthram Swami, Anna Scaglione
We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups…
Temporal Clustering in Dynamic Networks with Tensor Decomposition
Kun Tu, Bruno Ribeiro, Ananthram Swami +1
Dynamic networks are increasingly being usedd to model real world datasets. A challenging task in their analysis is to detect and characterize clusters. It is useful for analyzing…
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami +1
Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting…
Detection under Privileged Information
Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov +4
For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network…
Graph Wavelets via Sparse Cuts: Extended Version
Arlei Silva, Xuan-Hong Dang, Prithwish Basu +2
Modeling information that resides on vertices of large graphs is a key problem in several real-life applications, ranging from social networks to the Internet-of-things. Signal Pro…
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow +3
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing…