41 citations · 108 across the 8 of their papers we have counts for
8 papers
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Mark D. Risser, Marcus M. Noack, Hengrui Luo +1
The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeli…
BEACON -- Automated Aberration Correction for Scanning Transmission Electron Microscopy using Bayesian Optimization
Alexander J. Pattison, Stephanie M. Ribet, Marcus M. Noack +6
Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized…
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
Marcus M. Noack, Hengrui Luo, Mark D. Risser
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of…
A substitutional quantum defect in WS discovered by high-throughput computational screening and fabricated by site-selective STM manipulation
John C. Thomas, Wei Chen, Yihuang Xiong +21
Point defects in two-dimensional materials are of key interest for quantum information science. However, the space of possible defects is immense, making the identification of high…
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
Marcus M. Noack, Harinarayan Krishnan, Mark D. Risser +1
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to t…
Autonomous Investigations over WS and Au{111} with Scanning Probe Microscopy
John C. Thomas, Antonio Rossi, Darian Smalley +14
Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling sp…
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes
Marcus M. Noack, James A. Sethian
Gaussian process regression is a widely-applied method for function approximation and uncertainty quantification. The technique has gained popularity recently in the machine learni…
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels
Marcus M. Noack, Gregory S. Doerk, Ruipeng Li +4
A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no…