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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 491–500 of 813 papers

TitleStatusHype
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—0
Predicting Physical Object Properties from Video—0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture—0
Dynamic Split Computing for Efficient Deep Edge Intelligence—0
Nothing makes sense in deep learning, except in the light of evolution—0
Hyperparameter Optimization with Neural Network Pruning—0
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization—0
Hyper-Learning for Gradient-Based Batch Size Adaptation—0
Hybrid quantum ResNet for car classification and its hyperparameter optimization—0
Region-to-region kernel interpolation of acoustic transfer function with directional weighting—0
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