Minimal Implementation of Bayesian Optimization in JAX
-
Updated
May 8, 2024 - Python
Minimal Implementation of Bayesian Optimization in JAX
constrained/unconstrained multi-objective bayesian optimization package.
Differentiable Gaussian Process implementation for PyTorch
Surrogate Final BH properties
SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
Sparse Spectrum Gaussian Process Regression
Code and data accompanying our work on spatio-thermal depth correction of RGB-D sensors based on Gaussian Process Regression in real-time.
Gaussian Process Regression for training data with noisy inputs and/or outputs
Personal reimplementation of some ML algorithms for learning purposes
Multi Kernel Linear Mixed Models for Complex Phenotype Prediction
Bayesian Inference. Parallel implementations of DREAM, DE-MC and DRAM.
Gaussian process regression with feature selection
Modelling stellar activity signals with Gaussian process regression networks
Infinite-width neural networks from a practical point of view
Gaussian process regression-based adversarial image detection
Gaussian Process Regression vs. Relevance Vector Machine.
Hierarchical Gaussian Processes based Multi-Robot Relative Localization
Contribution to an open source repository which implements the Bayesian Optimization algorithm - Knowledge Gradient implementation
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
Official implementation of Self-Distillation for Gaussian Processes
Add a description, image, and links to the gaussian-process-regression topic page so that developers can more easily learn about it.
To associate your repository with the gaussian-process-regression topic, visit your repo's landing page and select "manage topics."