Search Results for author:
Found 8 papers, 3 papers with code
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
To significantly reduce the communication volume without compromising prediction accuracy, we propose a policy for caching data associated with frequently accessed vertices in remote partitions.
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
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 of computation and data movement due to the exponential growth of multi-hop graph neighborhoods along network layers.
Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution
We translate, encode and implement the principles in the platform with novel use of advanced concepts and techniques to ensure and protect data integrity and research integrity.
Computers and Society
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
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 images.
Spaceland Embedding of Sparse Stochastic Graphs
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 transformed into, a metric space.
Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering
The method is distinguished by the guiding principle to obtain a profile that is block-sparse with dense blocks.
Iterative Inversion of Deformation Vector Fields with Feedback Control
Conclusion: Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control.
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions
We present a new and effective approach for Hyperspectral Image (HSI) classification and clutter detection, overcoming a few long-standing challenges presented by HSI data characteristics.
