9 citations · 9 across the 3 of their papers we have counts for
5 papers
MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation
Amanda Muyskens, Benjamin Priest, Imène Goumiri +1
Gaussian processes (GPs) are non-linear probabilistic models popular in many applications. However, naïve GP realizations require quadratic memory to store the covariance matrix an…
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…
Simultaneous feedback control of toroidal magnetic field and plasma current on MST using advanced programmable power supplies
I. R. Goumiri, K. J. McCollam, A. A. Squitieri +3
Programmable control of the inductive electric field enables advanced operations of reversed-field pinch (RFP) plasmas in the Madison Symmetric Torus (MST) device and further devel…
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…