145 Open Source Gaussian Processes Software Projects

Machine learning, in numpy

A Python implementation of global optimization with gaussian processes.

A highly efficient and modular implementation of Gaussian Processes in PyTorch

Gaussian processes in TensorFlow

Fast and Easy Infinite Neural Networks in Python

Notebooks about Bayesian methods for machine learning

An easy and fast library to apply gaussian blur filter on any images. 🎩

Kriging Toolkit for Python

Fast and flexible Gaussian Process regression in Python

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

Python code for bayesian optimization using Gaussian processes

Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

Probabilistic Programming with Gaussian processes in Julia

Scala Library/REPL for Machine Learning Research

Bayesian Reinforcement Learning in Tensorflow

A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

A simple, extensible library for developing AutoML systems

Scalable 1D Gaussian Processes in C++, Python, and Julia

Gaussian Process Motion Planner 2

A lightweight framework for Gaussian processes and Bayesian optimization of black-box functions (C++-11)

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. Now almost entirely superseded by the models-by-example repo.

This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis

A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Safe reinforcement learning with stability guarantees

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Surrogate Optimization Toolbox for Python

Fast & scalable MCMC for all your exoplanet needs!

Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB

GPstuff - Gaussian process models for Bayesian analysis

Gaussian Process package based on data augmentation, sparsity and natural gradients

Safe Bayesian Optimization

Gaussian process modelling in Python

Learning-aided 3D mapping

Python library for Bayesian hyper-parameters optimization

Sparse Gaussian Processes for SLAM

My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]

Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326

Python3 project applying Gaussian process regression for forecasting stock trends

Deep convolutional gaussian processes.

A Visual Exploration of Gaussian Processes

Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)

This is a Project Page of 'Towards a complete 3D morphable model of the human head'

A library of scalable Bayesian generalised linear models with fancy features

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

Parallel Hyperparameter Tuning in Python

Julia package for kernel functions for machine learning

Library for doing GPR (Gaussian Process Regression) in OCaml. Comes with a command line application.

Bayesian optimization for Julia

Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX

Dimensionality reduction of spikes trains

Multi-View Stereo by Temporal Nonparametric Fusion

Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Reference implementation of Optimistic Expected Improvement.

Machine Learning Library, written in J

A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.

Max-value Entropy Search for Efficient Bayesian Optimization

a deep recurrent model for exchangeable data

This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.

Code for AutoGP

Combining tree-boosting with Gaussian process and mixed effects models

An experimental deep learning & genotype network-based system for predicting new influenza protein sequences.

A Python package for approximate Bayesian inference and optimization using Gaussian processes

Tools for data-driven spectra models with Gaussian processes. Pronounced "soap."

Non-stationary spectral mixture kernels implemented in GPflow

Python library to implement advanced trading strategies using machine learning and perform backtesting.

Variational Inference for Langevin Equations

Comparison of Variational Autoencoders with Bayesian Neural Networks. Accuracy, Latent space, Reconstruction and White Noise filtering.

constrained/unconstrained multi-objective bayesian optimization package.

"Learning Machine Learning" Course, Bogotá, Colombia 2019 #LML2019

customized GPflow with simple Tensorflow API

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Differentiable Gaussian Process implementation for PyTorch

Unifying sparse approximations for Gaussian process regression and classification, using Power EP

Fast inference for Gaussian processes in problems involving time

K2 systematics correction using Gaussian processes

multivariate Gaussian process regression and multivariate Student-t process regression

Library for Deep Gaussian Processes based on GPflow

Gaussian-Processes Surrogate Optimisation in python

Package for fitting Gaussian Process Emulators to multiple output computer simulation results.

Official code for "Efficient Deep Gaussian Process Models for Variable-Sized Inputs" - accepted in IJCNN2019

OpenRAVE plugin for GPMP2

Clustering with Gaussian Process Models

Implementation for Non-stationary Spectral Kernels (NIPS 2017)

Curated materials for different machine learning related summer schools

Conjugate gradients minimization

C++ Gaussian Process Library

Multi-Information Source Optimization

A Rust implementation of Gaussian Process regression.

Exoplanet detection in RVs with DNest4 and GPs

pyrff: Python implementation of random fourier feature approximations for gaussian processes

Code and data accompanying our work on spatio-thermal depth correction of RGB-D sensors based on Gaussian Process Regression in real-time.

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

Fast Gaussian process occupancy maps (GPOM) for dynamic environments using Big Data GP

Gaussian processes and Bayesian optimization for images and hyperspectral data

The Trendiness of Trends

Abstract types and methods for Gaussian Processes.

Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model