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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 471–480 of 813 papers

TitleStatusHype
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization—0
Demystifying Hyperparameter Optimization in Federated Learning—0
Transfer Learning for Bayesian HPO with End-to-End Meta-Features—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationCode2
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOCode1
Evaluating Transferability of BERT Models on Uralic LanguagesCode0
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