Image de couverture de livre

GP+: A Python Library for Kernel-based learning via Gaussian Processes

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Yousefpour, Amin, Foumani, Zahra Zanjani, Shishehbor, Mehdi, Mora, Carlos, Bostanabad, Ramin
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Constrained multi-fidelity Bayesian optimization with automatic stop condition
par: Foumani, Zahra Zanjani, et autres
Publié: (2025)

Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian Processes
par: Yousefpour, Amin, et autres
Publié: (2024)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
par: Mora, Carlos, et autres
Publié: (2024)

Operator Learning with Gaussian Processes
par: Mora, Carlos, et autres
Publié: (2024)

Compliance Minimization via Physics-Informed Gaussian Processes
par: Sun, Xiangyu, et autres
Publié: (2025)

Parametric Encoding with Attention and Convolution Mitigate Spectral Bias of Neural Partial Differential Equation Solvers
par: Shishehbor, Mehdi, et autres
Publié: (2024)

Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
par: Amiri, Mahsa, et autres
Publié: (2024)

Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization
par: Yousefpour, Amin, et autres
Publié: (2025)

Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
par: Sun, Xiangyu, et autres
Publié: (2026)

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
par: Negarandeh, Nima, et autres
Publié: (2025)

A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion
par: Vendrell-Gallart, Oriol, et autres
Publié: (2025)

Learning Mappings in Mesh-based Simulations
par: Hosseinmardi, Shirin, et autres
Publié: (2025)

HiGP: A high-performance Python package for Gaussian Process
par: Huang, Hua, et autres
Publié: (2025)

dynoGP: Deep Gaussian Processes for dynamic system identification
par: Benavoli, Alessio, et autres
Publié: (2025)

Should We Simultaneously Calibrate Multiple Computer Models?
par: Eweis-Labolle, Jonathan Tammer, et autres
Publié: (2025)

Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach
par: Mirshekari, Shahin, et autres
Publié: (2024)

EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems
par: Chen, Jianhong, et autres
Publié: (2025)

DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
par: Abu-Shaira, Mohammad, et autres
Publié: (2025)

Application of Long-Short Term Memory and Convolutional Neural Networks for Real-Time Bridge Scour Prediction
par: Hashem, Tahrima, et autres
Publié: (2024)

Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction
par: Yousefpour, Negin, et autres
Publié: (2024)

On the Uncertainty Quantification Ability of Tabular Foundation Models
par: Johnson, Tyler R., et autres
Publié: (2026)

Structured Kernel Regression VAE: A Computationally Efficient Surrogate for GP-VAEs in ICA
par: Wei, Yuan-Hao, et autres
Publié: (2025)

Spectral Anatomy of Quantum Gaussian Process Kernels
par: Xu, Jian, et autres
Publié: (2026)

Scalable Deep Basis Kernel Gaussian Processes
par: Zhu, Yunqin, et autres
Publié: (2025)

Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
par: Zou, Lu, et autres
Publié: (2024)

Expressive Mortality Models through Gaussian Process Kernels
par: Ludkovski, Mike, et autres
Publié: (2023)

High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
par: Camaño, Chris, et autres
Publié: (2024)

Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
par: Li, Jialin, et autres
Publié: (2024)

A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
par: Noack, Marcus M., et autres
Publié: (2023)

The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection
par: Islam, Md Shafiqul, et autres
Publié: (2026)

Revisiting Kernel Attention with Correlated Gaussian Process Representation
par: Bui, Long Minh, et autres
Publié: (2025)

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
par: Feng, Xiaodong, et autres
Publié: (2025)

Projection-Free Transformers via Gaussian Kernel Attention
par: Kundu, Debarshi, et autres
Publié: (2026)

Tensor Network-Constrained Kernel Machines as Gaussian Processes
par: Wesel, Frederiek, et autres
Publié: (2024)

DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes
par: Bolgár, Bence, et autres
Publié: (2025)

Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
par: Gilbert, Anna C., et autres
Publié: (2024)

Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations
par: Sendera, Marcin, et autres
Publié: (2024)

Machine learning-based system reliability analysis with Gaussian Process Regression
par: Zhou, Lisang, et autres
Publié: (2024)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences
par: Kanagawa, Motonobu, et autres
Publié: (2025)

Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
par: Warrior, Kane, et autres
Publié: (2026)