Revolutionizing C-Index With Meta-Learning Hyperparameters

Learning rate and weight decay coefficients. Our algorithm dynamically generates two important hyperparameters for optimization: Learning rates and weight decay coefficients. Oct 27, 2020 · hyperparameter optimization is one of the main pillars of machine learning algorithms. A hyperband based algorithm that.

We use metalearning to inform the decision of whether to optimize hyperparameters based on expected performance improvement and computational cost. The reviewers generally agreed that this paper brings an important contribution to the neurips community. The experiments are thorough. The results are quite strong, and.