GPR
47 papers with code • 0 benchmarks • 1 datasets
Gaussian Process Regression
Benchmarks
These leaderboards are used to track progress in GPR
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Most implemented papers
Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet
We simulate numerous GPR data from a range of pseudo‐random velocity models and feed the datasets into GPRNet for training.
Empirical Models for Multidimensional Regression of Fission Systems
a-jd/npsn
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Findings from this work establish guidelines for developing empirical models for multidimensional regression of neutron transport.
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
ivam-he/BernNet
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NeurIPS 2021
Many representative graph neural networks, e. g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters.
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
In this paper, we present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts in the target variable scale of seasonal data.
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability
Albeit the tremendous performance improvements in designing complex artificial intelligence (AI) systems in data-intensive domains, the black-box nature of these systems leads to the lack of trustworthiness.
Modelling Arbitrary Complex Dielectric Properties -- an automated implementation for gprMax
There is a need to accurately simulate materials with complex electromagnetic properties when modelling Ground Penetrating Radar (GPR), as many objects encountered with GPR contain water, e. g. soils, curing concrete, and water-filled pipes.
Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties
AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials.
Satellite galaxy abundance dependency on cosmology in Magneticum simulations
Conclusions: This work provides a preliminary calibration of the cosmological dependency of the satellite abundance of high mass halos, and we showed that modelling HOD with cosmological parameters is necessary to interpret satellite abundance, and we showed the importance of using FP simulations in modelling this dependency.
Machine Learning Based Forward Solver: An Automatic Framework in gprMax
General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems.
Gaussian Process Regression With Interpretable Sample-Wise Feature Weights
yuyay/gpx
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IEEE Transactions on Neural Networks and Learning Systems 2021
In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model.
