GPR
47 papers with code • 0 benchmarks • 1 datasets
Gaussian Process Regression
Benchmarks
These leaderboards are used to track progress in GPR
Poser
Most implemented papers
An Intuitive Tutorial to Gaussian Process Regression
jwangjie/gaussian-process-regression-tutorial
•
•
This tutorial is accessible to a broad audience, including those new to machine learning, ensuring a clear understanding of GPR fundamentals.
Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning
To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent.
A Geometric Analysis of Phase Retrieval
complex Gaussian) and the number of measurements is large enough ($m \ge C n \log^3 n$), with high probability, a natural least-squares formulation for GPR has the following benign geometric structure: (1) there are no spurious local minimizers, and all global minimizers are equal to the target signal $\mathbf x$, up to a global phase; and (2) the objective function has a negative curvature around each saddle point.
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
weiziyoung/Predicting_brain_age
•
•
Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data.
Multivariate Gaussian and Student$-t$ Process Regression for Multi-output Prediction
Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction.
Fusion of hyperspectral and ground penetrating radar to estimate soil moisture
In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model.
Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data
polimi-ispl/landmine_detection_autoencoder
•
•
This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas.
Kernel Mode Decomposition and programmable/interpretable regression networks
kernelmodedec/Kernel-Mode-Decomposition-1D
•
•
Mode decomposition is a prototypical pattern recognition problem that can be addressed from the (a priori distinct) perspectives of numerical approximation, statistical inference and deep learning.
Bayesian Inference Gaussian Process Multiproxy Alignment of Continuous Signals (BIGMACS): Applications for Paleoceanography
We use BIGMACS to construct a new Deep Northeastern Atlantic stack (i. e., a profile from a particular benthic ${\delta}^{18}{\rm O}$ records) of five ocean sediment cores.
