4 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…
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…
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…