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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 311–320 of 813 papers

TitleStatusHype
Flying By ML -- CNN Inversion of Affine Transforms—0
A Hessian-informed hyperparameter optimization for differential learning rate—0
Flexora: Flexible Low Rank Adaptation for Large Language Models—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
From Random Search to Bandit Learning in Metric Measure Spaces—0
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization—0
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
GANs and alternative methods of synthetic noise generation for domain adaption of defect classification of Non-destructive ultrasonic testing—0
FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization—0
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis—0
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