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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 581–590 of 813 papers

TitleStatusHype
A Study of Genetic Algorithms for Hyperparameter Optimization of Neural Networks in Machine TranslationCode0
Hyperparameter Optimization via Sequential Uniform DesignsCode1
HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks—0
Sample-Efficient Automated Deep Reinforcement LearningCode1
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias AssessmentsCode1
Fast Approximate Multi-output Gaussian ProcessesCode0
Estimating the time-lapse between medical insurance reimbursement with non-parametric regression models—0
Efficient hyperparameter optimization by way of PAC-Bayes bound minimizationCode0
Black Magic in Deep Learning: How Human Skill Impacts Network TrainingCode0
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and PracticeCode2
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