An Empirical Bayes Approach to Optimizing Machine Learning Algorithms

NeurIPS 2017 James Mcinerney

There is rapidly growing interest in using Bayesian optimization to tune model and inference hyperparameters for machine learning algorithms that take a long time to run. For example, Spearmint is a popular software package for selecting the optimal number of layers and learning rate in neural networks... (read more)

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