Good PapersI 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
Stheno.jlProbabilistic Programming with Gaussian processes in Julia
PilcoBayesian Reinforcement Learning in Tensorflow
Keras GpKeras + Gaussian Processes: Learning scalable deep and recurrent kernels.
GpytorchA highly efficient and modular implementation of Gaussian Processes in PyTorch
Cornell MoeA Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
DynamlScala Library/REPL for Machine Learning Research
Gpmp2Gaussian Process Motion Planner 2
BtbA simple, extensible library for developing AutoML systems
LimboA lightweight framework for Gaussian processes and Bayesian optimization of black-box functions (C++-11)
CeleriteScalable 1D Gaussian Processes in C++, Python, and Julia
Miscellaneous R CodeCode 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 Now almost entirely superseded by the models-by-example repo.
Safe learningSafe reinforcement learning with stability guarantees
PysotSurrogate Optimization Toolbox for Python
AbolethA bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
VbmcVariational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
ExoplanetFast & scalable MCMC for all your exoplanet needs!
NasbotNeural Architecture Search with Bayesian Optimisation and Optimal Transport
SthenoGaussian process modelling in Python
BcpdBayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available
GpstuffGPstuff - Gaussian process models for Bayesian analysis
GpflowGaussian processes in TensorFlow
GaussianprocessesPython3 project applying Gaussian process regression for forecasting stock trends
La3dmLearning-aided 3D mapping
Neural Kernel NetworkCode for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
Deep Kernel GpDeep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
Ts EmoThis repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
Gp Infer NetScalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
Epinow2Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
Ipynotebook machinelearningThis contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
GaussianblurAn easy and fast library to apply gaussian blur filter on any images. 🎩
GeorgeFast and flexible Gaussian Process regression in Python
periodicityUseful tools for periodicity analysis in time series data.
k2scK2 systematics correction using Gaussian processes
modelsForecasting 🇫🇷 elections with Bayesian statistics 🥳
FBNNCode for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
hyper-enginePython library for Bayesian hyper-parameters optimization
mnist-challengeMy solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
lgprR-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.
models-by-exampleBy-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
Stheno.jlProbabilistic Programming with Gaussian processes in Julia
GPimGaussian processes and Bayesian optimization for images and hyperspectral data
Universal Head 3DMMThis is a Project Page of 'Towards a complete 3D morphable model of the human head'
random-fourier-featuresImplementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
brunoa deep recurrent model for exchangeable data