21 citations · 26 across the 8 of their papers we have counts for
8 papers
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski +3
Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in…
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Tim Kaler, Nickolas Stathas, Anne Ouyang +4
Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot…
Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution
Dimitris Floros, Mulugu V. Brahmajothi, Alexandros-Stavros Iliopoulos +2
Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of many interactive neural and humoral biomarkers in order to establish predictive, diagnostic, and therapeu…
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
Wanyi Fu, Shobhit Sharma, Ehsan Abadi +6
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical imag…
Spaceland Embedding of Sparse Stochastic Graphs
Nikos Pitsianis, Alexandros-Stavros Iliopoulos, Dimitris Floros +1
We introduce a nonlinear method for directly embedding large, sparse, stochastic graphs into low-dimensional spaces, without requiring vertex features to reside in, or be transform…
Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering
Nikos Pitsianis, Dimitris Floros, Alexandros-Stavros Iliopoulos +3
Calculation of near-neighbor interactions among high dimensional, irregularly distributed data points is a fundamental task to many graph-based or kernel-based machine learning alg…