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Hyperparameter Optimization

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Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

코드

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

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/uber/ludwig

Personalized Benchmarking with the Ludwig Benchmarking Toolkit

https://github.com/ucr-optml/fedmsa

Optimizing Millions of Hyperparameters by Implicit Differentiation

https://github.com/uma-pi1/dist-kge

Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings

https://github.com/weiyadi/dlm_sgp

Direct loss minimization algorithms for sparse Gaussian processes

https://github.com/wujian16/qKG

A Tutorial on Bayesian Optimization

https://github.com/xiao7199/compare_gan

Are GANs Created Equal? A Large-Scale Study

https://github.com/yangyucheng000/Paper-2/tree/main/SharpDRO-ms-main

sharpDARTS: Faster and More Accurate Differentiable Architecture Search

https://github.com/yangyucheng000/University/tree/main/model-2/gpt_bigcode

GPT Takes the Bar Exam

https://github.com/yanpei18345156216/BayesianOptimization

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

https://github.com/yiwang89/fmfn-BayesianOptimization

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

https://github.com/yiyuezhuo/A-Tutorial-on-Bayesian-Optimization-exmaple

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

https://github.com/yoomambo/BayesianOptimization_Tuned

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

https://github.com/yqian4/optuna

Optuna: A Next-generation Hyperparameter Optimization Framework

https://github.com/zatang007/BayesianOptimization

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

https://github.com/zygmuntz/hyperband

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

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