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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 301–310 of 813 papers

TitleStatusHype
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in TransformersCode0
Simple Hack for Transformers against Heavy Long-Text Classification on a Time- and Memory-Limited GPU Service—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties—0
Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis—0
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship TablesCode0
Adaptive Hyperparameter Optimization for Continual Learning Scenarios—0
Hyperparameter Tuning MLPs for Probabilistic Time Series ForecastingCode0
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