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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/dbkgroup/GVAE4Smiles

Grammar Variational Autoencoder

https://github.com/destiny301/dnc

Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning

https://github.com/dylancashman/remap_nas

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/eric-erki/autokeras

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/eric-erki/robotics-rl-srl

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/erichanslee/lookahead_release

Efficient Rollout Strategies for Bayesian Optimization

https://github.com/evensgn/hyperbo-mphd

Pre-trained Gaussian Processes for Bayesian Optimization

https://github.com/facebookresearch/how-to-autorl

Bayesian Generational Population-Based Training

https://github.com/facebookresearch/la-mcts

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

https://github.com/facebookresearch/mobile-vision

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

https://github.com/facebookresearch/nevergrad

Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020

https://github.com/feji3769/VecchiaBO

Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes

https://github.com/finefun0/autokeras

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/fmfn/BayesianOptimization

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

https://github.com/freedombenLiu/autokeras

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/frehbach/rehb20a

Expected Improvement versus Predicted Value in Surrogate-Based Optimization

https://github.com/gaetanserre/stochastic-global-optimization

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

https://github.com/geoffwoollard/learn_cryoem_math

Alignment of Density Maps in Wasserstein Distance

https://github.com/goktug97/bohb-hpo

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

https://github.com/gowthamasu/Coverage_based_sample_design

Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization

https://github.com/hangyuzhu/leakage-attack-in-federated-learning

Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage

https://github.com/harveydevereux/Mondrian

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

https://github.com/hcllaw/distBO

Hyperparameter Learning via Distributional Transfer

https://github.com/herilalaina/mosaic_ml

Automated Machine Learning with Monte-Carlo Tree Search

https://github.com/hhyqhh/Relation

Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis

https://github.com/hiit/elfi

ELFI: Engine for Likelihood-Free Inference

https://github.com/huawei-noah/noah-research/tree/master/HEBO

HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation

https://github.com/hughsalimbeni/bayesian_benchmarks

Differentiable Compositional Kernel Learning for Gaussian Processes

https://github.com/hynkis/MIHO

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/hynkis/mi-hpo

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/j-groeneveld/covid

Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

https://github.com/j-wilson/MaximizingAcquisitionFunctions

Maximizing acquisition functions for Bayesian optimization

https://github.com/jaayala/constrained_bayes_opt

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics

https://github.com/jamesrobertlloyd/automl-phase-2

Freeze-Thaw Bayesian Optimization

https://github.com/jbrea/BayesianOptimization.jl

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

https://github.com/jekyllstein/HORDOpt.jl

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

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