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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

Title	Date	Tasks	Status	Hype
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning	Dec 12, 2010	Bayesian OptimizationHierarchical Reinforcement Learning	CodeCode Available	2
Conditional Neural Fields	Dec 1, 2009	Handwriting RecognitionHyperparameter Optimization	—Unverified	0
Rethinking LDA: Why Priors Matter	Dec 1, 2009	Hyperparameter OptimizationTopic Models	—Unverified	0

Title

Status

Hype

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

CodeCode Available

Conditional Neural Fields

—Unverified

Rethinking LDA: Why Priors Matter

—Unverified

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Hyperparameter Optimization

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