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Hyperparameter Optimization

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

Papers

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Showing 791–800 of 813 papers

TitleStatusHype
Efficient and Robust Automated Machine LearningCode3
Scalable Gradient-Based Tuning of Continuous Regularization HyperparametersCode0
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
No Regret Bound for Extreme Bandits—0
Learning Structural Kernels for Natural Language Processing—0
apsis - Framework for Automated Optimization of Machine Learning Hyper ParametersCode0
Non-stochastic Best Arm Identification and Hyperparameter OptimizationCode0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Gradient-based Hyperparameter Optimization through Reversible LearningCode0
Supplementary Material for Efficient and Robust Automated Machine LearningCode3
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