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Greatest papers with code
An Intuitive Tutorial to Gaussian Processes Regression
• jwangjie/An-Intuitive-Tutorial-to-Gaussian-Processes-Regression
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This tutorial aims to provide an intuitive understanding of the Gaussian processes regression.
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.
Adaptive Universal Generalized PageRank Graph Neural Network
ICLR 2021
• jianhao2016/GPRGNN
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We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information extraction, regardless of the extent to which the node labels are homophilic or heterophilic.
Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data
• polimi-ispl/landmine_detection_autoencoder
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This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas.
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.
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.
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.
Kernel Mode Decomposition and programmable/interpretable regression networks
• kernelmodedec/Kernel-Mode-Decomposition-1D
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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.
A Multiple Continuous Signal Alignment Algorithm with Gaussian Process Profiles and an Application to Paleoceanography
Aligning signals is essential for integrating fragmented knowledge in each signal or resolving signal classification problems.
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.