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GPR

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/max-veit/velociraptor

공식 코드

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

https://github.com/meganebers/discrepancy-modeling-framework-code

공식 코드

Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects

https://github.com/mile888/gp_cc-opf

공식 코드

Data-Driven Stochastic AC-OPF using Gaussian Processes

https://github.com/owen-ren0003/rshdmrgpr

공식 코드

Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method

https://github.com/qiqi-dai/3dinvnet

공식 코드

3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion

https://github.com/sgbaird-5DOF/interp

공식 코드

Five Degree-of-Freedom Property Interpolation of Arbitrary Grain Boundaries via Voronoi Fundamental Zone Octonion Framework

https://github.com/songc0a/fast-gpr-fwi

공식 코드

Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch

https://github.com/sunju/pr_plain

공식 코드

A Geometric Analysis of Phase Retrieval

https://github.com/suresh0807/PPSO

공식 코드

Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials

https://github.com/susyustc/bbmm

공식 코드

Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning

https://github.com/susyustc/kagpr

공식 코드

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression

https://github.com/syed-luqman-shah-19/gpr-ieeewcnc2025

공식 코드

Interference Prediction Using Gaussian Process Regression and Management Framework for Critical Services in Local 6G Networks

https://github.com/tejasanvekar/gpr-net

공식 코드

GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

https://github.com/thupchnsky/sgc_unlearn

공식 코드

Certified Graph Unlearning

https://github.com/unibas-gravis/gingr

공식 코드

GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression

https://github.com/utsav-akhaury/gprMax

공식 코드

Machine Learning Based Forward Solver: An Automatic Framework in gprMax

https://github.com/xiangyan93/molecules-enumerate

공식 코드

Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation

https://github.com/zhouchunpong/multi-view-3DGPR

공식 코드

Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR

https://github.com/zhouxr6066/Res-SAM

공식 코드

Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis

https://github.com/TejasAnvekar/Mahalanobis-k-NN

GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning

https://github.com/kernelmodedec/Kernel-Mode-Decomposition-1D

Kernel Mode Decomposition and programmable/interpretable regression networks

https://github.com/polimi-ispl/landmine_detection_autoencoder

Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data

https://github.com/ryojitanabe/ela_drframework

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

https://github.com/saferGPMLE/saferGPMLE

Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation

https://github.com/vr308/Generalised-Gaussian-Processes

Approximate Inference for Fully Bayesian Gaussian Process Regression

https://github.com/weiziyoung/Predicting_brain_age

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

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.