18 citations · 51 across the 17 of their papers we have counts for
5 papers · 1 filter
Star-Galaxy Separation via Gaussian Processes with Model Reduction
Imène R. Goumiri, Amanda L. Muyskens, Michael D. Schneider +2
Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Bein…
Scaling Graph Clustering with Distributed Sketches
Benjamin W. Priest, Alec Dunton, Geoffrey Sanders
The unsupervised learning of community structure, in particular the partitioning vertices into clusters or communities, is a canonical and well-studied problem in exploratory graph…
Quantum Machine Learning using Gaussian Processes with Performant Quantum Kernels
Matthew Otten, Imène R. Goumiri, Benjamin W. Priest +2
Quantum computers have the opportunity to be transformative for a variety of computational tasks. Recently, there have been proposals to use the unsimulatably of large quantum devi…
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels
Imène R. Goumiri, Benjamin W. Priest, Michael D. Schneider
While deep neural networks (DNNs) and Gaussian Processes (GPs) are both popularly utilized to solve problems in reinforcement learning, both approaches feature undesirable drawback…
DegreeSketch: Distributed Cardinality Sketches on Massive Graphs with Applications
Benjamin W. Priest
We present DegreeSketch, a semi-streaming distributed sketch data structure and demonstrate its utility for estimating local neighborhood sizes and local triangle count heavy hitte…