2 repositorios
Awesome GitHub RepositoriesAcquisition Function Balancing
Mathematical methods to manage the trade-off between exploring new search space and exploiting known high-value areas.
Distinct from Load Balancing Architectures: None of the candidates cover the specific probabilistic optimization concept of acquisition functions; distinct from network load balancing.
Explore 2 awesome GitHub repositories matching scientific & mathematical computing · Acquisition Function Balancing. Refine with filters or upvote what's useful.
Awesome Acquisition Function Balancing GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- fmfn/bayesianoptimization
fmfn/BayesianOptimization
8,650Ver en GitHubThis is a Python scientific computing library for finding the global maximum of expensive black-box functions. It operates as a global optimization framework that identifies optimal input parameters within defined bounds to maximize a target output. The library utilizes Gaussian process regression to predict function values and uncertainty, guiding the search for optimal parameters. It employs a surrogate-model optimization approach to approximate high-cost objective functions, reducing the total number of required evaluations. The system manages the trade-off between exploration and exploit
Implements mathematical formulas to balance exploration and exploitation during the search for optimal parameters.
Python - bayesian-optimization/bayesianoptimization
bayesian-optimization/BayesianOptimization
8,552Ver en GitHubThis is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling. The tool is specifically designed for hyperparameter tuning in machine learning, where it maximizes model performance while minimizing the number of required training runs. It treats the target function as a black box, selecting optimal input parameters based on statistical priors to reduce manual trial and error. The system utilizes G
Balances the exploration of uncertain regions and exploitation of known high-value areas using an acquisition function.
Pythonbayesian-optimizationgaussian-processesoptimization
