Reverse Dependencies of bayesian-optimization

The following projects have a declared dependency on bayesian-optimization:

• aideml — Autonomous AI for Data Science and Machine Learning
• aixd — AI-eXtended Design (AIXD)
• autobmt — a modeling tool that automatically builds scorecards and tree models.
• autogl — AutoML tools for graph-structure dataset
• autotreemodel — auto build a tree model
• BayesOpt4dftu — no summary
• beetroots — Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS)
• boela — Bayesian Optimization with Exploratory Landscape Analysis
• bonsai-tree — Bayesian Optimization + Gradient Boosted Trees
• china — description
• commonroad-geometric — Contains basic functionality for facilitating research on graph neural networks for autonomous driving and provides an interface between CommonRoad and Pytorch Geometric.
• crgeo — Contains basic functionality for facilitating research on graph neural networks for autonomous driving and provides an interface between CommonRoad and Pytorch Geometric.
• csle-agents — Reinforcement learning agents for CSLE
• didtool — Tool set for feature engineering & modeling
• Djaizz — Artificial Intelligence (AI) in Django Applications
• elastool — Elastic tool for zero and finite-temperature elastic constants and mechanical properties calculations
• empyric — A package for experiment automation
• factory-ai — no summary
• gemben — Benchmark for Graph Embedding Algorithms
• hana_automl — Welcome to hana_automl - Automated Machine Learning library based on SAP HANA.
• humpday — Taking the pain out of choosing a Python global optimizer
• InsurAutoML — Automated Machine Learning/AutoML pipeline.
• itlubber-automl — https://zhuanlan.zhihu.com/p/447307569
• iWork — description
• LightGBMwithBayesOpt — A Python toolkit of light gbm with bayesian optimizer.
• lightworks — Open-source Python SDK for photonic quantum computation.
• mbGDML — Create, use, and analyze machine learning potentials within the many-body expansion framework
• ML-Navigator — ML-Navigator is a tutorial-based Machine Learning framework. The main component of ML-Navigator is the flow. A flow is a collection of compact methods/functions that can be stuck together with guidance texts.
• mlrap — Machine Learning Regression Analyse Packages
• mutagene — Mutational analysis with Python
• muygpys — Scalable Approximate Gaussian Process using Local Kriging
• naludaq — Backend package for Nalu Scientific hardware
• nevergrad — A Python toolbox for performing gradient-free optimization
• orpheus-ml — A package for automated ML model training and creation of pipelines capable of handling multiple estimators.
• otx — OpenVINO™ Training Extensions: Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
• pydatafabric — SHINSEGAE DataFabric Python Package
• pyRDDLGym-jax — pyRDDLGym-jax: JAX compilation of RDDL description files, and a differentiable planner in JAX.
• PYSNN — Framework for engineering and simulating spiking neural networks, built on top of PyTorch.
• python-mlboardclient — Ml-Board Client Library
• pytorch-mppi — Model Predictive Path Integral (MPPI) implemented in pytorch
• quantfolio — A small example package
• scikit-physlearn — A machine learning library for regression.
• scmcallib — Perform calibration for simple climate models
• scorecardzxh — scorecard modeling tools
• siatune — no summary
• skga — The python package implementing the HyperBRKGA algorithm optimizes hyperparameters of machine learning algorithms through a hybrid approach based on genetic algorithms.
• skt — SKT package
• slickml — SlickML: Slick Machine Learning in Python
• TopasOpt — optimisation for topas Monte Carlo
• tql-Python — description
• tune-easy — tune-easy: A hyperparameter tuning tool, extremely easy to use.
• ubc-solar-simulation — UBC Solar's Simulation Environment
• wale-net — Prediction module for CommonRoad
• wavpool — A network block with built in spacial and scale decomposition.
• Yikai-helper-funcs — Test nbdev for developing packages for self-resue
• yotse — Your Optimization Tool for Scientific Experiments
• Yuan — description