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Found 2 papers, 2 papers with code
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
Providing a model that achieves a strong predictive performance and at the same time is interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives.
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values.
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