69 citations · 239 across the 21 of their papers we have counts for
6 papers · 1 filter
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
Guixiang Ma, Nesreen K. Ahmed, Ted Willke +4
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic dist…
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek +4
Magnetic resonance imaging is capable of producing volumetric images without ionizing radiation. Nonetheless, long acquisitions lead to prohibitively long exams. Compressed sensing…
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu +3
As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-d…
Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs
Linpeng Tang, Yida Wang, Theodore L. Willke +1
For a deep learning model, efficient execution of its computation graph is key to achieving high performance. Previous work has focused on improving the performance for individual…
Temporo-Spatial Collaborative Filtering for Parameter Estimation in Noisy DCE-MRI Sequences: Application to Breast Cancer Chemotherapy Response
Xia Zhu, Dipanjan Sengupta, Andrew Beers +2
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally invasive imaging technique which can be used for characterizing tumor biology and tumor response to ra…
Learning Role-based Graph Embeddings
Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee +4
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features…