Learning-aided 3D mapping
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Updated
Nov 24, 2023 - C++
Learning-aided 3D mapping
The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.
A minimal implementation of Gaussian process regression in PyTorch
Surrogate Final BH properties
constrained/unconstrained multi-objective bayesian optimization package.
Library for doing GPR (Gaussian Process Regression) in OCaml. Comes with a command line application.
A step-by-step guide for surrogate optimization using Gaussian Process surrogate model
Sparse Spectrum Gaussian Process Regression
Python module providing a framework to trace individual edges in an image using Gaussian process regression.
Multi Kernel Linear Mixed Models for Complex Phenotype Prediction
SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
Code and data accompanying our work on spatio-thermal depth correction of RGB-D sensors based on Gaussian Process Regression in real-time.
Gaussian Process Regression vs. Relevance Vector Machine.
Bayesian Inference. Parallel implementations of DREAM, DE-MC and DRAM.
Recursive Gaussian Process regression allows performing GP regression, while also being able to add train the model at runtime
Differentiable Gaussian Process implementation for PyTorch
Gaussian process regression with feature selection
Interpolate grain boundary properties in a 5 degree-of-freedom sense via a novel distance metric.
A review of python packages for Gaussian Process Regression
Modern C++ library handling gaussian processes
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