18 citations · 37 across the 9 of their papers we have counts for
10 papers
The Reported Engagement with AI Level (REAL) Rating: A Framework for Disclosing Human-AI Collaboration
Imène Goumiri, Mayleen Cortez-Rodriguez, Eric Bell +1
Generative artificial intelligence has become increasingly incorporated into digital media and more generally into production workflows with which the public frequently interacts.…
Correspondence of NNGP Kernel and the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Kernels representing limiting cases of neural network architectures have recently gained popularity. However, the application and performance of these new kernels compared to exist…
Identifiability and Sensitivity Analysis of Kriging Weights for the Matern Kernel
Amanda Muyskens, Benjamin W. Priest, Imene R. Goumiri +1
Gaussian process (GP) models are effective non-linear models for numerous scientific applications. However, computation of their hyperparameters can be difficult when there is a la…
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)
Imène R. Goumiri, Alec M. Dunton, Amanda L. Muyskens +2
Temporal variations of apparent magnitude, called light curves, are observational statistics of interest captured by telescopes over long periods of time. Light curves afford the e…
Star-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification
Amanda L. Muyskens, Imène R. Goumiri, Benjamin W. Priest +4
We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in h…
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…