126 citations · 186 across the 10 of their papers we have counts for
11 papers
Graph Vulnerability and Robustness: A Survey
Scott Freitas, Diyi Yang, Srijan Kumar +2
The study of network robustness is a critical tool in the characterization and sense making of complex interconnected systems such as infrastructure, communication and social netwo…
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
Omar Shaikh, Jon Saad-Falcon, Austin P Wright +4
The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As…
MalNet: A Large-Scale Image Database of Malicious Software
Scott Freitas, Rahul Duggal, Duen Horng Chau
Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation. These binary images are fast to gen…
A Large-Scale Database for Graph Representation Learning
Scott Freitas, Yuxiao Dong, Joshua Neil +1
With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the str…
Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browsers
Siwei Li, Zhiyan Zhou, Anish Upadhayay +7
Graph data have become increasingly common. Visualizing them helps people better understand relations among entities. Unfortunately, existing graph visualization tools are primaril…
ELF: An Early-Exiting Framework for Long-Tailed Classification
Rahul Duggal, Scott Freitas, Sunny Dhamnani +2
The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples. This long-tail causes classifiers to overfit to the major…
Evaluating Graph Vulnerability and Robustness using TIGER
Scott Freitas, Diyi Yang, Srijan Kumar +2
Network robustness plays a crucial role in our understanding of complex interconnected systems such as transportation, communication, and computer networks. While significant resea…
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
Scott Freitas, Shang-Tse Chen, Zijie J. Wang +1
Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to medical diagnosis. However, recent research has dem…
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
Rahul Duggal, Scott Freitas, Cao Xiao +2
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep lear…
D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks
Scott Freitas, Andrew Wicker, Duen Horng Chau +1
Given a large enterprise network of devices and their authentication history (e.g., device logons), how can we quantify network vulnerability to lateral attack and identify at-risk…
Local Partition in Rich Graphs
Scott Freitas, Hanghang Tong, Nan Cao +1
Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g. people) and their connective edges (e.g. interactio…