Results 11 to 20 of about 731,480 (301)
Spectral band selection for vegetation properties retrieval using Gaussian processes regression [PDF]
International Journal of Applied Earth Observation and Geoinformation, 2016 With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables.
J. Verrelst +5 more
semanticscholar +3 more sources
Gaussian Process Regression for Materials and Molecules [PDF]
Chemical Reviews, 2021 We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework ...
Volker L Deringer +2 more
exaly +6 more sources
Complex Gaussian Processes for Regression [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued one. This approach is limited. Based on the results in complex-valued linear theory and Gaussian random processes,
Rafael Boloix-Tortosa +2 more
exaly +5 more sources
Gaussian process regression for geometry optimization [PDF]
Journal of Chemical Physics, 2018 We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures.
Alexander Denzel, Johannes Kästner
exaly +3 more sources
Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities
Nature Reviews Physics, 2021 M. Noack +22 more
semanticscholar +3 more sources
Edge Tracing Using Gaussian Process Regression [PDF]
IEEE Transactions on Image Processing, 2022 15 pages, 6 figures. Accepted to be published in IEEE Transactions on Image Processing.
Jamie Burke, Stuart King
openaire +3 more sources
Variational Tobit Gaussian Process Regression
Statistics and Computing, 2023 AbstractWe propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires specialized techniques to perform inference since the resulting probabilistic models are typically ...
Marno Basson +2 more
openaire +2 more sources
Nonlinear Channel Equalization Using Gaussian Processes Regression in IMDD Fiber Link
IEEE Photonics Journal, 2022 Gaussian processes regression (GPR)-aided nonlinear channel equalizer (CE) is experimentally demonstrated in a multi-level intensity modulation and direct detection fiber link.
Xiang Li +4 more
doaj +1 more source
Fast methods for training Gaussian processes on large datasets [PDF]
Royal Society Open Science, 2016 Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing ...
C. J. Moore +3 more
doaj +1 more source
Manifold Gaussian Processes for regression [PDF]
2016 International Joint Conference on Neural Networks (IJCNN), 2016 26.03.14 KB.
Calandra, R +3 more
openaire +7 more sources