122 Open source projects that are alternatives of or similar to Ts Emo

Python library for Bayesian hyper-parameters optimization

Surrogate Optimization Toolbox for Python

Parallel Hyperparameter Tuning in Python

GPstuff - Gaussian process models for Bayesian analysis

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

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

Notebooks about Bayesian methods for machine learning

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

Gaussian processes and Bayesian optimization for images and hyperspectral data

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

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

A Python implementation of global optimization with gaussian processes.

Yes, it's a camel case.

The standard and parallel multiobjective EGO algorithms

Hyperparameter optimization in Julia.

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

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

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

AutoOED: Automated Optimal Experimental Design Platform

Probabilistic Programming with Gaussian processes in Julia

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

Automated Machine Learning with scikit-learn

Experimental Global Optimization Algorithm

Useful tools for periodicity analysis in time series data.

Interactive visualization of Gaussian processes

ytopt: machine-learning-based search methods for autotuning

Bayesian Optimization and Grid Search for xgboost/lightgbm

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

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

Sequential Gallery for Interactive Visual Design Optimization [SIGGRAPH 2020]

R-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.

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Fitness landscape exploration sandbox for biological sequence design.

Max-value Entropy Search for Efficient Bayesian Optimization

a distributed Hyperband implementation on Steroids

Tutorial on Bayesian optimization in R

Deodorant: Solving the problems of Bayesian Optimization

A minimal implementation of Gaussian process regression in PyTorch

Using Bayesian Optimization to optimize hyper parameter in Keras-made neural network model.

Estimate Realtime Case Counts and Time-varying Epidemiological Parameters

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

K2 systematics correction using Gaussian processes

Kriging Toolkit for Python

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

Phoenics: Bayesian optimization for efficient experiment planning

Learnable Embedding Space for Efficient Neural Architecture Compression

a deep recurrent model for exchangeable data

Forecasting 🇫🇷 elections with Bayesian statistics 🥳

The Trendiness of Trends

PHYSBO -- optimization tools for PHYsics based on Bayesian Optimization

Sequential Model-based Algorithm Configuration

Fast and flexible Gaussian Process regression in Python

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

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Combining tree-boosting with Gaussian process and mixed effects models

Generalized and Efficient Blackbox Optimization System [SIGKDD'21].

A didactic Gaussian process package for researchers in Jax.

Bayesian Optimization algorithms with various recent improvements