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Bayesian Optimization

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zr777777777777/bo-sa-pinns

공식 코드

BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers

https://gitlab.com/gpatom/ase-gpatom

공식 코드

Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentials

https://gitlab.com/s3anaz/multi-fidelity-cost-aware-bayesian-optimization

공식 코드

Multi-Fidelity Cost-Aware Bayesian Optimization

https://huggingface.co/spaces/herilalaina/ifbo

공식 코드

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization

https://github.com/0h-n0/tfdbonas

Scalable Bayesian Optimization Using Deep Neural Networks

https://github.com/0h-n0/thdbonas

Scalable Bayesian Optimization Using Deep Neural Networks

https://github.com/1Reinier/Reservoir

Efficient Optimization of Echo State Networks for Time Series Datasets

https://github.com/Abdulk084/Smiles2vec

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

https://github.com/Abhik1998/HIV-predictor

SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

https://github.com/Argaadya/intro-bayesian

Practical Bayesian Optimization of Machine Learning Algorithms

https://github.com/AriChow/error_propagation

Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines

https://github.com/BrutishGuy/ContinuumArmedBandits

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/ChangYong-Oh/HyperSphere

BOCK : Bayesian Optimization with Cylindrical Kernels

https://github.com/Data-Science-in-Mechanical-Engineering/Contextual-GoSafe

GoSafe: Globally Optimal Safe Robot Learning

https://github.com/EvgeniyBochenkov/github-move

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/GPflow/GPflowOpt

GPflowOpt: A Bayesian Optimization Library using TensorFlow

https://github.com/Gourhari/README

NeVAE: A Deep Generative Model for Molecular Graphs

https://github.com/IOHprofiler/Analyzer

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

https://github.com/IOHprofiler/IOHexperimenter

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

https://github.com/IOHprofiler/Post-Processing

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

https://github.com/JasperSnoek/spearmint

Practical Bayesian Optimization of Machine Learning Algorithms

https://github.com/JohnYKiyo/coco_trial

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/KernelTuner/kernel_tuner

Bayesian Optimization for auto-tuning GPU kernels

https://github.com/KlabCMU/ESNAC

Learnable Embedding Space for Efficient Neural Architecture Compression

https://github.com/LawrenceMMStewart/Bayesian_Optimization

Bayesian Optimization for Parameter Tuning of the XOR Neural Network

https://github.com/MLBazaar/AutoBazaar

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

https://github.com/MLBazaar/BTB

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

https://github.com/MLBazaar/MLBlocks

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

https://github.com/MLStruckmann/mutation-misery

On the Performance of Differential Evolution for Hyperparameter Tuning

https://github.com/Nathnos/Article-Reproduction-Deep-Learning-Efficient-Joint-Neural-Architecture-and-Hyperparameter-Search

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

https://github.com/Nikhil-Mukund/RIFF

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

https://github.com/P-Mikkola/PPBO

Projective Preferential Bayesian Optimization

https://github.com/PKU-DAIR/mindware

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

https://github.com/PlaytikaOSS/pybandits

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints

https://github.com/ProbabilisticNumerics/probabilistic_line_search

Probabilistic Line Searches for Stochastic Optimization

https://github.com/PurityFan/bohb_in_nni

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.