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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 321–330 of 813 papers

TitleStatusHype
Breaking MLPerf Training: A Case Study on Optimizing BERT—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble—0
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
Adaptive Regret for Bandits Made Possible: Two Queries Suffice—0
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence AnalysisCode0
Hypercomplex neural network in time series forecasting of stock data—0
Flying By ML -- CNN Inversion of Affine Transforms—0
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