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156 search results found

D2l En ⭐ 20,613

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Numpy Ml ⭐ 14,162

Machine learning, in numpy

Bayesianoptimization ⭐ 7,481

A Python implementation of global optimization with gaussian processes.

Gpytorch ⭐ 3,337

A highly efficient implementation of Gaussian Processes in PyTorch

Gpflow ⭐ 1,783

Gaussian processes in TensorFlow

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Pykrige ⭐ 670

Kriging Toolkit for Python

A machine learning course using Python, Jupyter Notebooks, and OpenML

Gpboost ⭐ 486

Combining tree-boosting with Gaussian process and mixed effects models

Fast and flexible Gaussian Process regression in Python

Gaussian processes in JAX.

Keras Gp ⭐ 219

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

Bayesian Optimization ⭐ 218

Python code for bayesian optimization using Gaussian processes

Bayesian Reinforcement Learning in Tensorflow

Gaussian process modelling in Python

Bayesnewton ⭐ 196

Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.

Celerite ⭐ 177

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

Gaussian Processes for Experimental Sciences

Deep Kernel Transfer ⭐ 142

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

Miscellaneous R Code ⭐ 140

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 Now almost entirely superseded by the models-by-example repo.

sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model

Syn2real ⭐ 119

Syn2Real Transfer Learning for Image Deraining using Gaussian Processes

Safe_learning ⭐ 117

Safe reinforcement learning with stability guarantees

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Safeopt ⭐ 108

Safe Bayesian Optimization

PyVBMC: Variational Bayesian Monte Carlo algorithm for posterior and model inference in Python

Hilo Mpc ⭐ 92

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

Pycrop Yield Prediction ⭐ 80

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

Data Efficient Reinforcement Learning With Probabilistic Model Predictive Control ⭐ 76

Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments

Hyper Engine ⭐ 69

Python library for Bayesian hyper-parameters optimization

Random Fourier Features ⭐ 66

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

Mnist Challenge ⭐ 64

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

Neural Kernel Network ⭐ 62

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

Variational Fourier Features

Code for paper "Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning"

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

Gaussian processes and Bayesian optimization for images and hyperspectral data

Gaussian Process Model Building Interface

Gaussianprocesses ⭐ 47

Python3 project applying Gaussian process regression for forecasting stock trends

Deep Kernel Gp ⭐ 46

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

Graph Gaussian Processes ⭐ 41

Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".

Dimensionality reduction of spikes trains

Approxposterior ⭐ 38

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

Liestationarykernels ⭐ 36

Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"

Convnets As Gps ⭐ 36

Code for "Deep Convolutional Networks as shallow Gaussian Processes"

Mogp Emulator ⭐ 36

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

Gaussian processes with arbitrary derivative constraints and predictions.

Deterministic algorithms for objective Bayesian inference and hyperparameter optimization

De-trending of K2 Light curves

Bayesian Optimization ⭐ 31

Reference implementation of Optimistic Expected Improvement.

Rumour Classification ⭐ 30

Code to reproduce experiments from the EMNLP 2015 paper about Rumour Stance Classification with Gaussian Processes.

Structurally efficient multi-output linearly coregionalized Gaussian Processes: it's tricky, tricky, tricky, tricky, tricky.

Deepgp_approxep ⭐ 28

see https://github.com/thangbui/geepee for a faster implementation

A Python library for Robotic Information Gathering

a deep recurrent model for exchangeable data

Library for training Gaussian Processes on Molecules

Gpflowsampling ⭐ 25

Code for efficiently sampling functions from GP(flow) posteriors

Periodicity ⭐ 25

Useful tools for periodicity analysis in time series data.

Parametricgp ⭐ 24

Parametric Gaussian Process Regression for Big Data

Code for AutoGP

IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)

Autoforce ⭐ 23

Sparse Gaussian Process Potentials

Fulu is a python library of supernova light curves approximation methods based on machine learning.

Multi-View Stereo by Temporal Nonparametric Fusion

Active Bayesian Causal Inference ⭐ 21

Active Bayesian Causal Inference (Neurips'22)

Non-stationary spectral mixture kernels implemented in GPflow

Python package for Preference Learning with Gaussian Processes.

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

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

Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)

Python Scientific ⭐ 17

Quick guide and tutorial to scientific data python programming

A Python Toolkit for AGN Time Series Analysis using CARMA models

Generalised Gaussian Processes ⭐ 17

Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods

Sghmc_dgp ⭐ 16

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

Gpflow Slim ⭐ 16

customized GPflow with simple Tensorflow API

Gaussian-Processes Surrogate Optimisation in python

Pycones 2019 Data ⭐ 16

PyConES 2019 conferences, attachments and related stuff

Gecb_planner ⭐ 15

GECB planner: Gradient-Enhanced Path Generation and Quadratic Bezier-Based Path Smoothing for Unstructured Environments

Multi-fidelity modeling using Gaussian processes and nonlinear auto-regressive schemes.

Continualgp ⭐ 15

Continual Gaussian Processes

Rgbd Correction ⭐ 15

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

🔆 A Python implementation of a sum-product network with gaussian processes leafs model (SPNGP, arXiv:1809.04400) 📃

Probabilistic Response mOdel Fitting with Interactive Tools

Differentiable Gaussian Process implementation for PyTorch

Gaussian Online Processes for Python

Diverse Video Generation using a Gaussian Process Trigger

1D, super-resolution brightness profile reconstruction for interferometric sources

Categorical Latent Gaussian Process

Gaussian Processes for Sequential Data

Linpde Gp ⭐ 13

Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"

Gp_tutorial_pydata ⭐ 13

PyData San Luis 2017 Tutorial: An Introduction to Gaussian Processes in PyMC3

Stochastic state-space Inference and Prediction

Python package 'dgpsi' for deep and linked Gaussian process emulations

Gp_pref_elicit ⭐ 13

Ordered Preference Elicitation Strategies for Multi-Objective Decision Making using Gaussian Processes

Variational Auto-Regressive Gaussian Processes for Continual Learning

Deep Gaussian Process ⭐ 12

🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0

constrained/unconstrained multi-objective bayesian optimization package.

Gaussian processes on graphs and lattices in Stan and pytorch.

Gaussian_processes ⭐ 12

Python library for working with gaussian processes

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