32 citations · 71 across the 14 of their papers we have counts for
14 papers
Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining
Nathan Vaska, Justin Goodwin, Robin Walters +1
Meshes are used to represent complex objects in high fidelity physics simulators across a variety of domains, such as radar sensing and aerodynamics. There is growing interest in u…
GRASP: Accelerating Shortest Path Attacks via Graph Attention
Zohair Shafi, Benjamin A. Miller, Ayan Chatterjee +2
Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an e…
Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks
Zohair Shafi, Benjamin A. Miller, Tina Eliassi-Rad +1
Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems. We investigate the Set Cover Problem (SCP) and propose Graph-SC…
Antibody Representation Learning for Drug Discovery
Lin Li, Esther Gupta, John Spaeth +3
Therapeutic antibody development has become an increasingly popular approach for drug development. To date, antibody therapeutics are largely developed using large scale experiment…
System Analysis for Responsible Design of Modern AI/ML Systems
Virginia H. Goodwin, Rajmonda S. Caceres
The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective…
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces
Peter Morales, Rajmonda Sulo Caceres, Tina Eliassi-Rad
Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce lo…