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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 351–360 of 813 papers

TitleStatusHype
Fine-tune your Classifier: Finding Correlations With Temperature—0
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networksCode1
Trading Off Resource Budgets for Improved Regret Bounds—0
Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine—0
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer—0
PyHopper -- Hyperparameter optimizationCode1
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization—0
Sampling Streaming Data with Parallel Vector Quantization -- PVQ—0
Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruit—0
Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures—0
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