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88 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

Gpflow ⭐ 1,783

Gaussian processes in TensorFlow

Bayesian Machine Learning ⭐ 1,587

Notebooks about Bayesian methods for machine learning

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

Gpboost ⭐ 486

Combining tree-boosting with Gaussian process and mixed effects models

Gaussian processes in JAX.

Stheno.jl ⭐ 327

Probabilistic Programming with Gaussian processes in Julia

Parallel Hyperparameter Tuning in Python

Kernelfunctions.jl ⭐ 260

Julia package for kernel functions for machine learning

Good Papers ⭐ 231

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

Keras Gp ⭐ 219

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

Bayesian Reinforcement Learning in Tensorflow

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

Gaussian process modelling in Python

Scala Library/REPL for Machine Learning Research

Bayesnewton ⭐ 196

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

Cornell Moe ⭐ 165

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

Gaussian Processes for Experimental Sciences

Survival Analysis Using Deep Learning ⭐ 138

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

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

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

Bayesianoptimization.jl ⭐ 84

Bayesian optimization for Julia

Pycrop Yield Prediction ⭐ 80

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

Kalman Jax ⭐ 77

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

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

Deep convolutional gaussian processes.

Machine Learning Library, written in J

Bayesianml ⭐ 55

Experiments in Bayesian Machine Learning

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

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

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.

Deterministic algorithms for objective Bayesian inference and hyperparameter optimization

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

Bayesian Optimization ⭐ 31

Reference implementation of Optimistic Expected Improvement.

Autogp.jl ⭐ 30

Automated Bayesian model discovery for time series data

A Python library for Robotic Information Gathering

Max Value Entropy Search ⭐ 27

Max-value Entropy Search for Efficient Bayesian Optimization

Go Bayesopt ⭐ 25

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

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

Code for AutoGP

Flexible Bayesian Optimization in R

Autoforce ⭐ 23

Sparse Gaussian Process Potentials

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

Dissertation ⭐ 20

Zheng Zhao's doctoral dissertation from Aalto University

Variationalautoencoders ⭐ 17

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

Gpflow Slim ⭐ 16

customized GPflow with simple Tensorflow API

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

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

Ml_course ⭐ 15

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

Differentiable Gaussian Process implementation for PyTorch

Machine Learning Summer Schools ⭐ 14

Curated materials for different machine learning related summer schools

Diverse Video Generation using a Gaussian Process Trigger

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

Minimal Gaussian process library in JAX with a simple (custom) approach to state management.

C++ Gaussian Process Library

Nips2017 ⭐ 11

Multi-Information Source Optimization

Minimize ⭐ 11

Conjugate gradients minimization

State-space deep Gaussian processes in Python and Matlab

A software package for flexible HPC GPs

Multi Kernel Linear Mixed Models for Complex Phenotype Prediction

Functional Optimal Transport: Map Estimation and Domain Adaptation for Functional data

Time series anomaly detection via decomposition and gaussian process regression.

Csc2541 Ml Project ⭐ 9

Modeling Uncertainty in RNNs for Time Series Forecasting

Meetup Resources ⭐ 9

Collection of resources from each meetup event

🎶 A collection of études on probabilistic models.

A Minimalist Machine Learning Library written in Swift

Bayesian Methods For Machine Learning ⭐ 8

Bayesian Methods for Machine Learning

Chirp instantaneous frequency estimation using stochastic differential equation Gaussian processes

Interpolate grain boundary properties in a 5 degree-of-freedom sense via a novel distance metric.

Paccmann_gp ⭐ 7

PyTorch/skopt based implementation of Bayesian optimization with Gaussian processes - build to optimize latent spaces of VAEs to generate molecules with desired properties

Deepstructuredmixtures ⭐ 7

Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)

NUBO is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions developed by the Fluid Dynamics Lab at Newcastle University.

Incremental Gaussian Process Regression Igpr ⭐ 6

Winterconfhemavan2019 ⭐ 6

Material for my 'Bayesian Machine Learning' lectures at the Winter Conference in Statistics, Hemavan, Sweden, March 11-14, 2019.

Gpyopt Ml Agents ⭐ 6

Gaussian process optimization using GPyOpt for Unity ML-Agents Toolkit

Sdd Gp Mpc ⭐ 6

This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).

Sparse Spectrum Gaussian Process Regression

Official Implementation of "Doubly Mixed-Effects Gaussian Process Regression" (Jun Ho Yoon, Daniel P. Jeong, Seyoung Kim)

Bayesian Optimization using Gaussian Processes + web interface with result visualizations

Python Machine Learning Models ⭐ 5

Barebones Python implementations of machine learning models, without using machine learning libraries

Netflix Customer Retention Using Gpr ⭐ 5

Forecasting Netflix Customer Retention based on Gaussian Process Regression

Gaussian Process tiny explorer

Robust Gaussian Process with Iterative Trimming

Kfestimate.jl ⭐ 5

Julia package for KF and EKF parameter estimation using Automatic Differentiation

Mastercurves ⭐ 5

Python package for automatically superimposing data sets to create a master curve, using Gaussian process regression and maximum a posteriori estimation.

Contextual Gaussian Process Bandit Optimization ⭐ 5

Simple implementation of CGP-UCB algorithm.

GomPlex: Complex Gaussian Process for Machine Learning

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