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# An Intuitive Tutorial to Gaussian Processes Regression

22 Sep 2020jwangjie/Gaussian-Processes-Regression-Tutorial

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

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# Adaptive Universal Generalized PageRank Graph Neural Network

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.

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# Multivariate Gaussian and Student$-t$ Process Regression for Multi-output Prediction

13 Mar 2017Magica-Chen/gptp_multi_output

Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction.

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# Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data

2 Oct 2018polimi-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.

8

# Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

8 Dec 2016weiziyoung/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.

5

# A Geometric Analysis of Phase Retrieval

22 Feb 2016sunju/pr_plain

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.

5

# Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods

14 Sep 2019LiuHaiTao01/GPCnoise

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space.

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# Kernel Mode Decomposition and programmable/interpretable regression networks

19 Jul 2019kernelmodedec/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.

2

# Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

27 Mar 2020max-veit/velociraptor

In this work, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression (SA-GPR) framework, which assigns a (vector) dipole moment to each atom, while movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom.

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# A Multiple Continuous Signal Alignment Algorithm with Gaussian Process Profiles and an Application to Paleoceanography

20 Jul 2019eilion/DPGP-Stack

Aligning signals is essential for integrating fragmented knowledge in each signal or resolving signal classification problems.

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