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/paolorampazzo/mypySOT

pySOT and POAP: An event-driven asynchronous framework for surrogate optimization

https://github.com/phossen/autokeras

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/pipilurj/BONAS

Scalable Bayesian Optimization Using Deep Neural Networks

https://github.com/rdturnermtl/bbo_challenge_starter_kit

A Tutorial on Bayesian Optimization

https://github.com/rednotion/parallel_esn_web

Bayesian optimization of hyper-parameters in reservoir computing

https://github.com/resibots/limbo

Limbo: A Fast and Flexible Library for Bayesian Optimization

https://github.com/resibots/pautrat_2018_mlei

Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

https://github.com/rmcantin/bayesopt

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

https://github.com/ruocwang/mixture-of-prompts

InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models

https://github.com/ryojitanabe/ela_drframework

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

https://github.com/saferGPMLE/saferGPMLE

Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation

https://github.com/saitcakmak/contextual_rs

Bayesian Optimization of Risk Measures

https://github.com/scorebo/scorebo

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

https://github.com/sdewaele/EstimateSDE.jl

Accurate Characterization of Non-Uniformly Sampled Time Series using Stochastic Differential Equations

https://github.com/secondmind-labs/bayesian_benchmarks

Differentiable Compositional Kernel Learning for Gaussian Processes

https://github.com/shalijiang/bo

BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design

https://github.com/shir994/L-GSO

Black-Box Optimization with Local Generative Surrogates

https://github.com/sisl/BOMCP.jl

Bayesian Optimized Monte Carlo Planning

https://github.com/sparks-baird/crabnet-hyperparameter

Optimizing Training Trajectories in Variational Autoencoders via Latent Bayesian Optimization Approach

https://github.com/ssydasheng/Neural-Kernel-Network

Differentiable Compositional Kernel Learning for Gaussian Processes

https://github.com/sudeepsalgia/gp_threds

A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance

https://github.com/suyeecav/Bayesian-Optimization-for-Classifier-Evasion

Query-limited Black-box Attacks to Classifiers

https://github.com/svedel/greattunes

Max-value Entropy Search for Efficient Bayesian Optimization

https://github.com/tally0818/HyperBand

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/tconejeros/autocnn

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/tetette/Bachelorthesis

Auto-Keras: An Efficient Neural Architecture Search System

https://github.com/thegaussians/hyperband-for-any-model

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/thjashin/spectral-stein-grad

Differentiable Compositional Kernel Learning for Gaussian Processes

https://github.com/tholiao/learning-morph-and-ctrl

Data-efficient Learning of Morphology and Controller for a Microrobot

https://github.com/thomas-young-2013/lite-bo

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

https://github.com/thomas-young-2013/mindware

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

https://github.com/thomas-young-2013/open-box

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

https://github.com/thomas-young-2013/soln-ml

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

https://github.com/thuijskens/scikit-hyperband

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

https://github.com/toniesteves/cnn-bayesian-optimization

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

https://github.com/tsudalab/PrefInt

Leveraging Legacy Data to Accelerate Materials Design via Preference Learning

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