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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 201–210 of 813 papers

TitleStatusHype
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Explainable Bayesian OptimizationCode0
A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization—0
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence AnalysisCode0
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
Hypercomplex neural network in time series forecasting of stock data—0
Efficient Hyperparameter Optimization with Adaptive Fidelity IdentificationCode1
Flying By ML -- CNN Inversion of Affine Transforms—0
Tuning the activation function to optimize the forecast horizon of a reservoir computer—0
Provably Convergent Federated Trilevel Learning—0
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