Papers with Code 아카이브

Bayesian Optimization

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

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/Daniil-Selikhanovych/bnn-vi

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

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/Future-House/ldp

GAUCHE: A Library for Gaussian Processes in Chemistry

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/OxfordML/bayesquad

Batch Selection for Parallelisation of Bayesian Quadrature

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

https://github.com/QUVA-Lab/HyperSphere

BOCK : Bayesian Optimization with Cylindrical Kernels

https://github.com/S4iz/beta

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/SK-tklab/BayesianOptimization

Max-value Entropy Search for Efficient Bayesian Optimization

https://github.com/SK-tklab/RandomFourierFeatures

Batched Large-scale Bayesian Optimization in High-dimensional Spaces

https://github.com/Selozhd/UngatedRNN

The Statistical Recurrent Unit

https://github.com/SheffieldML/GPyOpt

Batch Bayesian Optimization via Local Penalization

https://github.com/Somya-Patra/Shell_ai

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

https://github.com/VirgiAgl/DAG-GP

Multi-task Causal Learning with Gaussian Processes

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