GPR
47 papers with code • 0 benchmarks • 1 datasets
Gaussian Process Regression
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Explainable Learning with Gaussian Processes
When using integrated gradients as an attribution method, we show that the attributions of a GPR model also follow a Gaussian process distribution, which quantifies the uncertainty in attribution arising from uncertainty in the model.
Data-Driven Stochastic AC-OPF using Gaussian Processes
To solve the non-convex and computationally challenging CC AC-OPF problem, the proposed approach relies on a machine learning Gaussian process regression (GPR) model.
Graph Neural Networks with Diverse Spectral Filtering
jingweio/dsf
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Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts.
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
zhouchunpong/multi-view-3DGPR
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To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
damnull/lagcn
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Also, humans have brain regions dedicated to understanding the minds of others and analyzing their intentions, such as the medial prefrontal cortex of the temporal lobe.
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity.
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning
In the realm of 3D-computer vision applications, point cloud few-shot learning plays a critical role.
Self-Distillation for Gaussian Process Regression and Classification
kennethborup/gaussian_process_self_distillation
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We propose two approaches to extend the notion of knowledge distillation to Gaussian Process Regression (GPR) and Gaussian Process Classification (GPC); data-centric and distribution-centric.
Robust and Scalable Gaussian Process Regression and Its Applications
This enables the application of Gaussian processes to a wide range of real data, which are often large-scale and contaminated by outliers.
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation
We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost.
