# GPR

33 papers with code • 0 benchmarks • 1 datasets

Gaussian Process Regression

## Benchmarks

These leaderboards are used to track progress in GPR
## Most implemented papers

# 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.

# An Intuitive Tutorial to Gaussian Processes Regression

This tutorial aims to provide an intuitive understanding of the Gaussian processes regression.

# 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

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

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

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.